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.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
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.control-group{margin-bottom:10px}
legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
.form-horizontal .control-group:after{clear:both}
.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
.form-horizontal .help-block{margin-bottom:0}
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.form-horizontal .form-actions{padding-left:180px}
table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
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.table th{font-weight:bold}
.table thead th{vertical-align:bottom}
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.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
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.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
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.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
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.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
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.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
.table tbody tr.success>td{background-color:#dff0d8}
.table tbody tr.error>td{background-color:#f2dede}
.table tbody tr.warning>td{background-color:#fcf8e3}
.table tbody tr.info>td{background-color:#d9edf7}
.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
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.icon-glass{background-position:0 0}
.icon-music{background-position:-24px 0}
.icon-search{background-position:-48px 0}
.icon-envelope{background-position:-72px 0}
.icon-heart{background-position:-96px 0}
.icon-star{background-position:-120px 0}
.icon-star-empty{background-position:-144px 0}
.icon-user{background-position:-168px 0}
.icon-film{background-position:-192px 0}
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.icon-signal{background-position:-408px 0}
.icon-cog{background-position:-432px 0}
.icon-trash{background-position:-456px 0}
.icon-home{background-position:0 -24px}
.icon-file{background-position:-24px -24px}
.icon-time{background-position:-48px -24px}
.icon-road{background-position:-72px -24px}
.icon-download-alt{background-position:-96px -24px}
.icon-download{background-position:-120px -24px}
.icon-upload{background-position:-144px -24px}
.icon-inbox{background-position:-168px -24px}
.icon-play-circle{background-position:-192px -24px}
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.icon-headphones{background-position:-336px -24px}
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.icon-fire{background-position:-72px -120px}
.icon-eye-open{background-position:-96px -120px}
.icon-eye-close{background-position:-120px -120px}
.icon-warning-sign{background-position:-144px -120px}
.icon-plane{background-position:-168px -120px}
.icon-calendar{background-position:-192px -120px}
.icon-random{background-position:-216px -120px;width:16px}
.icon-comment{background-position:-240px -120px}
.icon-magnet{background-position:-264px -120px}
.icon-chevron-up{background-position:-288px -120px}
.icon-chevron-down{background-position:-313px -119px}
.icon-retweet{background-position:-336px -120px}
.icon-shopping-cart{background-position:-360px -120px}
.icon-folder-close{background-position:-384px -120px;width:16px}
.icon-folder-open{background-position:-408px -120px;width:16px}
.icon-resize-vertical{background-position:-432px -119px}
.icon-resize-horizontal{background-position:-456px -118px}
.icon-hdd{background-position:0 -144px}
.icon-bullhorn{background-position:-24px -144px}
.icon-bell{background-position:-48px -144px}
.icon-certificate{background-position:-72px -144px}
.icon-thumbs-up{background-position:-96px -144px}
.icon-thumbs-down{background-position:-120px -144px}
.icon-hand-right{background-position:-144px -144px}
.icon-hand-left{background-position:-168px -144px}
.icon-hand-up{background-position:-192px -144px}
.icon-hand-down{background-position:-216px -144px}
.icon-circle-arrow-right{background-position:-240px -144px}
.icon-circle-arrow-left{background-position:-264px -144px}
.icon-circle-arrow-up{background-position:-288px -144px}
.icon-circle-arrow-down{background-position:-312px -144px}
.icon-globe{background-position:-336px -144px}
.icon-wrench{background-position:-360px -144px}
.icon-tasks{background-position:-384px -144px}
.icon-filter{background-position:-408px -144px}
.icon-briefcase{background-position:-432px -144px}
.icon-fullscreen{background-position:-456px -144px}
.dropup,.dropdown{position:relative}
.dropdown-toggle{*margin-bottom:-3px}
.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
.dropdown .caret{margin-top:8px;margin-left:2px}
.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
.open{*z-index:1000}.open>.dropdown-menu{display:block}
.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
.pull-right>.dropdown-menu{right:0;left:auto}
.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
.dropdown-submenu{position:relative}
.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
.dropdown-submenu:hover>.dropdown-menu{display:block}
.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
.dropdown-submenu:hover>a:after{border-left-color:#fff}
.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
.btn:active,.btn.active{background-color:#ccc \9}
.btn:first-child{*margin-left:0}
.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
.btn-block+.btn-block{margin-top:5px}
input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
.btn-primary:active,.btn-primary.active{background-color:#039 \9}
.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
.btn-success:active,.btn-success.active{background-color:#408140 \9}
.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
.btn-info:active,.btn-info.active{background-color:#24748c \9}
.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
.btn-group+.btn-group{margin-left:5px}
.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-group>.btn+.btn{margin-left:-1px}
.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
.btn-group>.btn-mini{font-size:9.75px}
.btn-group>.btn-small{font-size:11.049999999999999px}
.btn-group>.btn-large{font-size:16.25px}
.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
.btn .caret{margin-top:8px;margin-left:0}
.btn-large .caret{margin-top:6px}
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.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
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.alert h4{margin:0}
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.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
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.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}
.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}
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.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15);outline:0}
.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}
.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
.breadcrumb>.active{color:#999}
.pagination{margin:20px 0}
.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
.pagination ul>li{display:inline}
.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.pagination-centered{text-align:center}
.pagination-right{text-align:right}
.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
.pager:after{clear:both}
.pager li{display:inline}
.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
.pager .next>a,.pager .next>span{float:right}
.pager .previous>a,.pager .previous>span{float:left}
.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
.modal.fade.in{top:10%}
.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
.modal-header h3{margin:0;line-height:30px}
.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
.modal-form{margin-bottom:0}
.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
.modal-footer:after{clear:both}
.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
.modal-footer .btn-group .btn+.btn{margin-left:-1px}
.modal-footer .btn-block+.btn-block{margin-left:0}
.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
.tooltip.top{margin-top:-3px;padding:5px 0}
.tooltip.right{margin-left:3px;padding:0 5px}
.tooltip.bottom{margin-top:3px;padding:5px 0}
.tooltip.left{margin-left:-3px;padding:0 5px}
.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
.popover.right{margin-left:10px}
.popover.bottom{margin-top:10px}
.popover.left{margin-left:-10px}
.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
.popover-content{padding:9px 14px}
.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
.popover .arrow{border-width:11px}
.popover .arrow:after{border-width:10px;content:""}
.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
.thumbnails:after{clear:both}
.row-fluid .thumbnails{margin-left:0}
.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
.thumbnail .caption{padding:9px;color:#555}
.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
.media,.media .media{margin-top:15px}
.media:first-child{margin-top:0}
.media-object{display:block}
.media-heading{margin:0 0 5px}
.media>.pull-left{margin-right:10px}
.media>.pull-right{margin-left:10px}
.media-list{margin-left:0;list-style:none}
.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
.label:empty,.badge:empty{display:none}
a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
.label-important,.badge-important{background-color:#b94a48}
.label-important[href],.badge-important[href]{background-color:#953b39}
.label-warning,.badge-warning{background-color:#f89406}
.label-warning[href],.badge-warning[href]{background-color:#c67605}
.label-success,.badge-success{background-color:#468847}
.label-success[href],.badge-success[href]{background-color:#356635}
.label-info,.badge-info{background-color:#3a87ad}
.label-info[href],.badge-info[href]{background-color:#2d6987}
.label-inverse,.badge-inverse{background-color:#333}
.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
.btn .label,.btn .badge{position:relative;top:-1px}
.btn-mini .label,.btn-mini .badge{top:0}
@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
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.hero-unit li{line-height:30px}
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.visible-phone{display:none !important}
.visible-tablet{display:none !important}
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.visible-desktop{display:inherit !important}
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<h1 id="decision-trees">Decision Trees</h1>
<ul>
<li><p>Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.</p></li>
<li><p>Decision tree learning is a method commonly used in data mining.[</p></li>
<li><p>The goal is to create a model that predicts the value of a target variable based on several input variables.</p></li>
<li><p>Decision tree learning is one of the most successful techniques for supervised classification learning.</p></li>
<li>Decision trees generate white-box classification and regression models which can be used for <em>feature selection</em> and <em>sample prediction</em>.
<ul>
<li>(If a given situation is observable in a model the explanation for the condition is easily explained by boolean logic.)</li>
</ul></li>
<li><p>Decision trees require almost no data preparation (i.e. normalization) and can handle both numerical and nominal/categorical data. Decision trees can also be pruned or bundled into ensembles of trees (i.e. random forests) in order to remedy over-fitting and improve prediction accuracy.</p></li>
<li><p>A decision tree is a predictive model which maps observations (features) about an item to conclusions about the item’s type or class.</p></li>
<li><p>Large amounts of data can be analysed using standard computing resources in reasonable time.</p></li>
</ul>
<p>This library provides an implementation of the <a href="https://en.wikipedia.org/wiki/ID3_algorithm"><strong>ID3</strong></a> decision tree classifier algorithm along with:</p>
<ul>
<li>pruning, parallelized random forest generation</li>
<li>cross validation functionality</li>
<li>support for mixed numerical and nominal data.</li>
</ul>
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<div class="highlight"><pre><span class="n">using</span> <span class="n">DecisionTree</span>
<span class="n">using</span> <span class="n">RDatasets</span>
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<h4 id="using-iris-dataset-as-a-sample">Using Iris Dataset as a Sample</h4>
<p>The Iris dataset consists of 150 samples of <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set">iris flowers</a>, along with their:</p>
<ul>
<li>sepal lengths and widths</li>
<li>petal lengths and widths</li>
<li>specie names.</li>
</ul>
<p>We’ll use the sepal and petal measurements as the features, and the specie names (Setosa, Versicolor, Virginica) as the labels.</p>
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<div class="highlight"><pre><span class="n">iris</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">(</span><span class="s">"datasets"</span><span class="p">,</span> <span class="s">"iris"</span><span class="p">);</span>
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<div class="highlight"><pre><span class="c"># helper function to convert PooledDataArray to Array</span>
<span class="n">function</span> <span class="n">array</span><span class="p">{</span><span class="n">T</span><span class="p">,</span> <span class="n">R</span><span class="p">}(</span><span class="n">da</span><span class="p">::</span><span class="n">PooledDataArray</span><span class="p">{</span><span class="n">T</span><span class="p">,</span> <span class="n">R</span><span class="p">})</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">da</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">Array</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">size</span><span class="p">(</span><span class="n">da</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mi">1</span><span class="p">:</span><span class="n">n</span>
<span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">refs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">zero</span><span class="p">(</span><span class="n">R</span><span class="p">)</span>
<span class="n">error</span><span class="p">(</span><span class="n">NAException</span><span class="p">())</span>
<span class="k">else</span>
<span class="n">res</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">pool</span><span class="p">[</span><span class="n">da</span><span class="o">.</span><span class="n">refs</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
<span class="n">end</span>
<span class="n">end</span>
<span class="k">return</span> <span class="n">res</span>
<span class="n">end</span>
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<pre>
array (generic function with 1 method)
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<div class="highlight"><pre><span class="n">using</span> <span class="n">Gadfly</span>
<span class="n">using</span> <span class="n">RDatasets</span>
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<div class="highlight"><pre><span class="n">plot</span><span class="p">(</span><span class="n">dataset</span><span class="p">(</span><span class="s">"datasets"</span><span class="p">,</span> <span class="s">"iris"</span><span class="p">),</span><span class="n">x</span><span class="o">=</span><span class="s">"SepalLength"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s">"SepalWidth"</span><span class="p">,</span> <span class="n">Geom</span><span class="o">.</span><span class="n">point</span><span class="p">)</span>
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<h3 id="decision-stumps">Decision Stumps</h3>
<p>A <a href="https://en.wikipedia.org/wiki/Decision_stump">decision stump</a> is the simplest decision tree, consisting of only one split and two classification bins (leaves).</p>
<p>The split it generates is the one with the most predictive power. This is done by traversing all the features and splitting the dataset into two subsets for every unique value per feature, then returning the split with the highest information gain (lowest dispersion of binned labels across the leaves).</p>
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<div class="highlight"><pre><span class="n">iris</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">,:]</span>
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4x5 DataFrame:
SepalLength SepalWidth PetalLength PetalWidth Species
[1,] 5.1 3.5 1.4 0.2 "setosa"
[2,] 4.9 3.0 1.4 0.2 "setosa"
[3,] 4.7 3.2 1.3 0.2 "setosa"
[4,] 4.6 3.1 1.5 0.2 "setosa"
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<div class="highlight"><pre><span class="n">features</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">iris</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]);</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">array</span><span class="p">(</span><span class="n">iris</span><span class="p">[:,</span> <span class="s">"Species"</span><span class="p">]);</span>
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<div class="highlight"><pre><span class="n">stump</span> <span class="o">=</span> <span class="n">build_stump</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">features</span><span class="p">)</span>
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Decision Tree
Leaves: 2
Depth: 1
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<div class="highlight"><pre><span class="n">print_tree</span><span class="p">(</span><span class="n">stump</span><span class="p">)</span>
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Feature 3, Threshold 3.0
L-> setosa : 50/50
R-> versicolor : 50/100
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<p>The resultant split was made on the 3rd feature (petal length) with a threshold of 3.0 cm. Iris samples with a petal length less than 3.0 cm are binned into the Setosa specie leaf (Left), and those greater than or equal to 3.0 cm are binned into the Versicolor leaf (Right).</p>
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<p><em>Note that the Left leaf consists of 50 samples, all of which have the Setosa label, while the Right leaf contains 100 samples, half of which have the Versicolor label. Since there are a total of 50 samples of each species, we can see that this initial split separated all the Setosa samples into one leaf, but bundled the Versicolor and Virginica samples into another.</em></p>
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<h4 id="verifying-a-split">Verifying a Split</h4>
<p>One way of verifying this split is by making species predictions based on the sample features using the generated decision stump and then comparing the predictions to the actual species labels using a <a href="https://de.wikipedia.org/wiki/Beurteilung_eines_Klassifikators#Wahrheitsmatrix:_Richtige_und_falsche_Klassifikationen">confusion matrix</a>.</p>
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<div class="highlight"><pre><span class="n">predictions</span> <span class="o">=</span> <span class="n">apply_tree</span><span class="p">(</span><span class="n">stump</span><span class="p">,</span> <span class="n">features</span><span class="p">);</span>
<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
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Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
50 0 0
0 50 0
0 50 0
Accuracy: 0.6666666666666666
Kappa: 0.49999999999999994
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<p>We can see that all Setosa samples were correctly classified, while the Viginica samples were incorrectly classified as Versicolor, resulting in an overall accuracy of <span class="math">\(\frac{2}{3}\)</span>.</p>
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<p>So now we have a method of separating the Setosa species from the others (via petal length), but how can we separate the remaining two species from each other?</p>
<p>We could manually remove all the Setosa samples from the original dataset and generate another decision stump on the set consisting of only Versicolor and Virginica samples.</p>
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<div class="highlight"><pre><span class="n">no_setosa_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="mi">51</span><span class="p">:</span><span class="n">end</span><span class="p">];</span>
<span class="n">no_setosa_features</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="mi">51</span><span class="p">:</span><span class="n">end</span><span class="p">,</span> <span class="p">:];</span>
<span class="n">stump2</span> <span class="o">=</span> <span class="n">build_stump</span><span class="p">(</span><span class="n">no_setosa_labels</span><span class="p">,</span> <span class="n">no_setosa_features</span><span class="p">);</span>
<span class="n">print_tree</span><span class="p">(</span><span class="n">stump2</span><span class="p">)</span>
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Feature 4, Threshold 1.8
L-> versicolor : 49/54
R-> virginica : 45/46
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<p>This time the most predictive split takes place on the 4th feature (the petal width) with a threshold of 1.8 cm. The split isn’t as clean as the first one, but the separation of the two species is acceptable.</p>
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<p>We are now in a position to join the two splits generated by the decision stumps by manually cascading them to form a decision tree which will separate all three species from each other.</p>
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<h3 id="automatic-cascading-of-splits">Automatic Cascading of Splits</h3>
<p>So far we've gone to through building a decision stump, manually removing the well separated samples from our labels and features, building a secondary decision stump on the remaining samples, and finally manually cascading the two splits to generate a decision tree.</p>
<p>Instead of all this we could streamline the entire process.</p>
<p>We could recursively split the dataset into subsets, until the resulting subsets correspond to pure leaves that contain samples of only one specie class.</p>
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<div class="highlight"><pre><span class="n">tree</span> <span class="o">=</span> <span class="n">build_tree</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">features</span><span class="p">);</span>
<span class="n">print_tree</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span>
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Feature 3, Threshold 3.0
L-> setosa : 50/50
R-> Feature 4, Threshold 1.8
L-> Feature 3, Threshold 5.0
L-> Feature 4, Threshold 1.7
L-> versicolor : 47/47
R-> virginica : 1/1
R-> Feature 4, Threshold 1.6
L-> virginica : 3/3
R-> Feature 1, Threshold 7.2
L-> versicolor : 2/2
R-> virginica : 1/1
R-> Feature 3, Threshold 4.9
L-> Feature 1, Threshold 6.0
L-> versicolor : 1/1
R-> virginica : 2/2
R-> virginica : 43/43
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<p>Note that all the leaves are pure and some of them contain only one sample. This is a fully grown tree, where every sample has been forced into a leaf using different feature thresholds. But this forcing of samples onto solo leaves will often lead to over-fitting, and that’s where the pruning of leaves will help.</p>
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<h3 id="leaf-pruning">Leaf Pruning</h3>
<p>Pruning is a method of making decision trees less prone to over-fitting, thus improving their accuracy, and in the process reducing their size and complexity. With pruning, splits with little predictive power are removed, producing more flexible, yet still accurate models.</p>
<p>The pruning scheme is a bottom-up (pessimistic) process, which needs to be repeated until no more merges can be made. Let’s get a count of the number of leaves in our tree generated in the previous section, prune it with a purity threshold of 90%, and get a count of the leaves of the new pruned tree:</p>
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<div class="highlight"><pre><span class="n">length</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span>
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9
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<div class="highlight"><pre><span class="n">pruned</span> <span class="o">=</span> <span class="n">prune_tree</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">);</span>
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8
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<p>This pruning cut off only one of the leaves, so let’s get more aggressive and use a 60% threshold, and see what it looks like:</p>
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<div class="highlight"><pre><span class="n">pruned</span> <span class="o">=</span> <span class="n">prune_tree</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">);</span>
<span class="n">length</span><span class="p">(</span><span class="n">pruned</span><span class="p">)</span>
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3
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<div class="highlight"><pre><span class="n">print_tree</span><span class="p">(</span><span class="n">pruned</span><span class="p">)</span>
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Feature 3, Threshold 3.0
L-> setosa : 50/50
R-> Feature 4, Threshold 1.8
L-> versicolor : 49/54
R-> virginica : 45/46
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<p>But how can we determine a good threshold to use?</p>
<p>One approach is to prune the original tree using a variety of different thresholds, take the number of leaves for each pruned tree, plot the two variables against each other, and look for the <em>elbow</em> or where the most dramatic drop occurs in the plot. Another approach is to use cross validation.</p>
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<h3 id="n-fold-cross-validation">N-Fold Cross Validation</h3>
<p>Cross validation is a model validation technique for assessing how well a model will generalize given an independent data set.</p>
<p>This typically involves breaking the source datasets into training and testing sets, where the training set is used for generating the model, and the testing set for measuring its prediction accuracy.</p>
<p><a href="https://de.wikipedia.org/wiki/Kreuzvalidierungsverfahren#Problemstellung">N-fold cross validation</a> randomly partitions the original set into N equal sized subsets. Each subset is used once as the testing set, while the remaining samples are used for training, thus resulting in N prediction performance measurements.</p>
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<div class="highlight"><pre><span class="n">purities</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">10</span><span class="p">);</span>
<span class="n">accuracies</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">length</span><span class="p">(</span><span class="n">purities</span><span class="p">));</span>
<span class="p">[</span><span class="n">accuracies</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">nfoldCV_tree</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">purities</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">10</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="mi">1</span><span class="p">:</span><span class="n">length</span><span class="p">(</span><span class="n">purities</span><span class="p">)]</span>
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Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
5 0 0
6 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
5 0 0
6 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 6 0
0 4 0
0 5 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 8 0
0 2 0
0 5 0
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
6 0 0
5 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 7 0
0 4 0
0 4 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 6 0
0 3 0
0 6 0
Accuracy: 0.2
Kappa: 0.0
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
7 0 0
4 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
7 0 0
5 0 0
Accuracy: 0.2
Kappa: 0.0
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
7 0 0
4 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Mean Accuracy: 0.24
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 7
0 0 6
0 0 2
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 8
0 0 4
0 0 3
Accuracy: 0.2
Kappa: 0.0
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 5
0 0 8
0 0 2
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
6 0 0
6 0 0
Accuracy: 0.2
Kappa: 0.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 7
0 0 5
0 0 3
Accuracy: 0.2
Kappa: 0.0
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
2 0 0
5 0 0
8 0 0
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 7
0 0 5
0 0 3
Accuracy: 0.2
Kappa: 0.0
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 3 0
0 3 0
0 9 0
Accuracy: 0.2
Kappa: 0.0
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 4 0
0 3 0
0 8 0
Accuracy: 0.2
Kappa: 0.0
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
5 0 0
6 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Mean Accuracy: 0.18666666666666665
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 6
0 0 7
0 0 2
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 9
0 0 4
0 0 2
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 5 0
0 4 0
0 6 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
2 0 0
4 0 0
9 0 0
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 5
0 0 8
0 0 2
Accuracy: 0.13333333333333333
Kappa: 0.0
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 5 0
0 3 0
0 7 0
Accuracy: 0.2
Kappa: 0.0
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 0 5
0 0 7
0 0 3
Accuracy: 0.2
Kappa: 0.0
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
6 0 0
5 0 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 5 0
0 4 0
0 6 0
Accuracy: 0.26666666666666666
Kappa: 0.0
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
0 4 0
0 3 0
0 8 0
Accuracy: 0.2
Kappa: 0.0
Mean Accuracy: 0.1933333333333333
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
8 0 0
0 2 0
0 5 0
Accuracy: 0.6666666666666666
Kappa: 0.48979591836734687
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 6 0
0 6 0
Accuracy: 0.6
Kappa: 0.375
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 0 5
0 0 4
Accuracy: 0.6666666666666666
Kappa: 0.5098039215686274
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 0 8
0 0 3
Accuracy: 0.4666666666666667
Kappa: 0.3181818181818182
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
8 0 0
0 0 4
0 0 3
Accuracy: 0.7333333333333333
Kappa: 0.5714285714285714
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 3 0
0 6 0
Accuracy: 0.6
Kappa: 0.4444444444444444
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 6 0
0 6 0
Accuracy: 0.6
Kappa: 0.375
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
2 0 0
1 0 6
0 0 6
Accuracy: 0.5333333333333333
Kappa: 0.2857142857142857
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 0 7
0 0 4
Accuracy: 0.5333333333333333
Kappa: 0.3636363636363636
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 2 0
0 7 0
Accuracy: 0.5333333333333333
Kappa: 0.38596491228070173
Mean Accuracy: 0.5933333333333333
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 0 5
0 0 4
Accuracy: 0.6666666666666666
Kappa: 0.5098039215686274
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 0 6
0 0 3
Accuracy: 0.6
Kappa: 0.4444444444444444
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
1 0 0
0 5 0
0 9 0
Accuracy: 0.4
Kappa: 0.12337662337662345
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 4 0
0 7 0
Accuracy: 0.5333333333333333
Kappa: 0.3636363636363636
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 4 0
0 4 0
Accuracy: 0.7333333333333333
Kappa: 0.5833333333333333
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 4 0
0 5 0
Accuracy: 0.6666666666666666
Kappa: 0.5098039215686274
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 4 0
0 7 0
Accuracy: 0.5333333333333333
Kappa: 0.3636363636363636
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 0 6
0 0 5
Accuracy: 0.6
Kappa: 0.4155844155844156
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 0 5
0 0 3
Accuracy: 0.6666666666666666
Kappa: 0.506578947368421
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
1 0 6
0 0 3
Accuracy: 0.5333333333333333
Kappa: 0.375
Mean Accuracy: 0.5933333333333333
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
0 5 0
0 1 4
Accuracy: 0.9333333333333333
Kappa: 0.9
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 5 0
0 0 6
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 4 1
0 0 4
Accuracy: 0.9333333333333333
Kappa: 0.8993288590604027
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 4 0
0 0 8
Accuracy: 1.0
Kappa: 1.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 2 0
0 0 7
Accuracy: 1.0
Kappa: 1.0
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 3 0
0 0 8
Accuracy: 1.0
Kappa: 1.0
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 8 0
0 2 2
Accuracy: 0.8666666666666667
Kappa: 0.765625
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 5 0
0 1 2
Accuracy: 0.9333333333333333
Kappa: 0.8928571428571429
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
1 5 0
0 0 3
Accuracy: 0.9333333333333333
Kappa: 0.8958333333333334
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 6 1
0 0 2
Accuracy: 0.9333333333333333
Kappa: 0.8936170212765958
Mean Accuracy: 0.9533333333333335
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
0 2 0
0 0 8
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
0 5 1
0 0 4
Accuracy: 0.9333333333333333
Kappa: 0.9
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 6 0
0 0 5
Accuracy: 1.0
Kappa: 1.0
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 4 0
0 0 4
Accuracy: 1.0
Kappa: 1.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 2 1
0 0 6
Accuracy: 0.9333333333333333
Kappa: 0.8936170212765958
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
1 7 0
0 2 1
Accuracy: 0.8
Kappa: 0.653846153846154
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 3 0
0 0 5
Accuracy: 1.0
Kappa: 1.0
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 7 0
0 0 4
Accuracy: 1.0
Kappa: 1.0
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
0 3 1
0 2 4
Accuracy: 0.8
Kappa: 0.7000000000000001
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 7 0
0 0 5
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 0.9466666666666667
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 6 0
0 0 3
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
1 2 0
0 1 5
Accuracy: 0.8666666666666667
Kappa: 0.7916666666666667
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 3 0
0 0 6
Accuracy: 1.0
Kappa: 1.0
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 5 0
0 0 7
Accuracy: 1.0
Kappa: 1.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 3 0
0 2 7
Accuracy: 0.8666666666666667
Kappa: 0.7826086956521741
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 7 0
0 1 4
Accuracy: 0.9333333333333333
Kappa: 0.8928571428571429
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
3 0 0
0 8 1
0 1 2
Accuracy: 0.8666666666666667
Kappa: 0.7619047619047619
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 3 1
0 1 4
Accuracy: 0.8666666666666667
Kappa: 0.7972972972972974
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
8 0 0
0 3 1
0 0 3
Accuracy: 0.9333333333333333
Kappa: 0.8905109489051095
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 5 1
0 0 3
Accuracy: 0.9333333333333333
Kappa: 0.8979591836734694
Mean Accuracy: 0.9266666666666667
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 1 2
0 1 5
Accuracy: 0.8
Kappa: 0.6808510638297872
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 3 1
0 0 7
Accuracy: 0.9333333333333333
Kappa: 0.8936170212765958
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
1 3 0
0 0 5
Accuracy: 0.9333333333333333
Kappa: 0.8972602739726028
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 5 0
0 0 3
Accuracy: 1.0
Kappa: 1.0
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
0 5 0
0 1 4
Accuracy: 0.9333333333333333
Kappa: 0.9
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 4 1
0 0 4
Accuracy: 0.9333333333333333
Kappa: 0.8993288590604027
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 9 0
0 1 1
Accuracy: 0.9333333333333333
Kappa: 0.8717948717948718
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 5 0
0 0 6
Accuracy: 1.0
Kappa: 1.0
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
2 0 0
0 4 1
0 0 8
Accuracy: 0.9333333333333333
Kappa: 0.8837209302325582
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 5 0
0 1 3
Accuracy: 0.9333333333333333
Kappa: 0.8979591836734694
Mean Accuracy: 0.9333333333333333
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 3 0
0 1 7
Accuracy: 0.9333333333333333
Kappa: 0.8936170212765958
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
7 0 0
0 4 0
0 0 4
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
5 0 0
0 6 0
0 0 4
Accuracy: 1.0
Kappa: 1.0
Fold 4
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
2 0 0
1 5 1
0 0 6
Accuracy: 0.8666666666666667
Kappa: 0.7887323943661971
Fold 5
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 4 1
0 1 3
Accuracy: 0.8666666666666667
Kappa: 0.7972972972972974
Fold 6
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 4 0
0 1 4
Accuracy: 0.9333333333333333
Kappa: 0.8993288590604027
Fold 7
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 2 1
0 1 5
Accuracy: 0.8666666666666667
Kappa: 0.7916666666666667
Fold 8
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
6 0 0
0 5 2
0 0 2
Accuracy: 0.8666666666666667
Kappa: 0.7945205479452055
Fold 9
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 3 0
0 0 8
Accuracy: 1.0
Kappa: 1.0
Fold 10
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
4 0 0
0 8 0
0 1 2
Accuracy: 0.9333333333333333
Kappa: 0.8854961832061069
Mean Accuracy: 0.9266666666666667
</pre>
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Out[46]:</div>
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<pre>
10-element Array{Float64,1}:
0.24
0.186667
0.193333
0.593333
0.593333
0.953333
0.946667
0.926667
0.933333
0.926667
</pre>
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<div class="input hbox">
<div class="prompt input_prompt">
In [52]:
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<div class="input_area box-flex1">
<div class="highlight"><pre><span class="n">using</span> <span class="n">PyPlot</span>
<span class="n">plot</span><span class="p">(</span><span class="n">purities</span><span class="p">,</span> <span class="n">accuracies</span><span class="p">,</span> <span class="s">"b-o"</span><span class="p">)</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s">"Purity Threshold"</span><span class="p">);</span> <span class="n">ylabel</span><span class="p">(</span><span class="s">"Accuracy"</span><span class="p">)</span>
</pre></div>
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<div class="vbox output_wrapper">
<div class="output vbox">
<div class="hbox output_area"><div class="prompt"></div>
<div class="box-flex1 output_subarea output_display_data">
<img 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">
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Out[52]:</div>
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<pre>
PyObject <matplotlib.text.Text object at 0x7fc1763c41d0>
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<p>Looks like we achieve high prediction performance and low model complexity at the 60% threshold.</p>
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</section><section>
<section>
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="random-forests">Random Forests</h3>
<p>Decision trees can be <em>bagged</em> together to create ensemble tree learners, one such type being the <a href="https://de.wikipedia.org/wiki/Random_Forest">random forest</a>.</p>
<p>Each tree in a random forest is trained on a bootstrapped sample of the original dataset, and for each split, only a randomly chosen subset of the features is considered. The trees are fully grown and not pruned, and the majority vote of their individual predictions is taken as the forest’s overall prediction.</p>
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<div class="text_cell_render border-box-sizing rendered_html">
<p>Random forests have the advantage of being less prone to over-fitting, and thus generalize better than individual decision trees. Another advantage they provide is in performance, as they don’t need to search through large feature spaces in their entirety for each split. But they lack the transparency offered by decision trees and are less interpretable.</p>
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In [28]:
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<div class="highlight"><pre><span class="n">forest</span> <span class="o">=</span> <span class="n">build_forest</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">);</span>
<span class="p">[</span><span class="n">length</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span> <span class="k">for</span> <span class="n">tree</span> <span class="ow">in</span> <span class="n">forest</span><span class="o">.</span><span class="n">trees</span><span class="p">]</span><span class="s">'</span>
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Out[28]:</div>
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<pre>
1x10 Array{Any,2}:
8 11 5 9 11 11 8 10 7 8
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<p>We can also use the model to make predictions and to perform 3-fold cross validation using the same arguments:</p>
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In [53]:
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<div class="highlight"><pre><span class="n">predictions</span> <span class="o">=</span> <span class="n">apply_forest</span><span class="p">(</span><span class="n">forest</span><span class="p">,</span> <span class="n">features</span><span class="p">);</span>
<span class="n">nfoldCV_forest</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">);</span>
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<pre>
Fold 1
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
18 0 0
0 15 1
0 0 16
Accuracy: 0.98
Kappa: 0.969951923076923
Fold 2
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
18 0 0
0 16 2
0 2 12
Accuracy: 0.92
Kappa: 0.8792270531400966
Fold 3
Classes: {"setosa","versicolor","virginica"}
Matrix: 3x3 Array{Int64,2}:
14 0 0
1 15 0
0 8 12
Accuracy: 0.82
Kappa: 0.7324613555291318
Mean Accuracy: 0.9066666666666666
</pre>
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</div></section>
</section><section>
<section>
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="resume">Resume</h3>
<ul>
<li><p>Apart from making predictions, decision trees can be used for feature selection.</p></li>
<li><p>By looking at the top and bottom splits of a trained tree, we can get a sense of which features are worth keeping and which ones to worth discarding, respectively.</p></li>
<li><p>This way, if the decision trees or random forests provide poor accuracy results for the dataset at hand, we could pass on the features with more predictive power to another type of classifier and potentially speed it up drastically.</p></li>
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</div>
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history: true,
theme: Reveal.getQueryHash().theme, // available themes are in /css/theme
transition: Reveal.getQueryHash().transition || 'linear', // default/cube/page/concave/zoom/linear/none
// Optional libraries used to extend on reveal.js
dependencies: [
{ src: "//cdn.jsdelivr.net/reveal.js/2.4.0/lib/js/classList.js", condition: function() { return !document.body.classList; } },
{ src: "//cdn.jsdelivr.net/reveal.js/2.4.0/plugin/highlight/highlight.js", async: true, callback: function() { hljs.initHighlightingOnLoad(); } },
{ src: "//cdn.jsdelivr.net/reveal.js/2.4.0/plugin/notes/notes.js", async: true, condition: function() { return !!document.body.classList; } }
// { src: 'http://s7.addthis.com/js/300/addthis_widget.js', async: true},
]
});
</script>
<!-- MathJax configuration -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
tex2jax: {
inlineMath: [ ['$','$'], ["\\(","\\)"] ],
displayMath: [ ['$$','$$'], ["\\[","\\]"] ]
},
displayAlign: 'left', // Change this to 'center' to center equations.
"HTML-CSS": {
styles: {'.MathJax_Display': {"margin": 0}}
}
});
</script>
<!-- End of mathjax configuration -->
<script>
// We wait for the onload function to load MathJax after the page is completely loaded.
// MathJax is loaded 1 unit of time after the page is ready.
// This hack prevent problems when you load multiple js files (i.e. social button from addthis).
//
window.onload = function () {
setTimeout(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML";
document.getElementsByTagName("head")[0].appendChild(script);
},1)
}
</script>
<script>
Reveal.addEventListener( 'slidechanged', function( event ) {
MathJax.Hub.Rerender(event.currentSlide);
});
</script>
</body>
</html>