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<!doctype html>
<html>
<head>
<meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'>
<title>Presentation</title><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color:#ffffff; --text-color:#333333; --select-text-bg-color:#B5D6FC; --select-text-font-color:auto; --monospace:"Lucida Console",Consolas,"Courier",monospace; }
html { font-size: 14px; background-color: var(--bg-color); color: var(--text-color); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -webkit-font-smoothing: antialiased; }
body { margin: 0px; padding: 0px; height: auto; bottom: 0px; top: 0px; left: 0px; right: 0px; font-size: 1rem; line-height: 1.42857; overflow-x: hidden; background: inherit; tab-size: 4; }
iframe { margin: auto; }
a.url { word-break: break-all; }
a:active, a:hover { outline: 0px; }
.in-text-selection, ::selection { text-shadow: none; background: var(--select-text-bg-color); color: var(--select-text-font-color); }
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#write.first-line-indent li { margin-left: 2em; }
.for-image #write { padding-left: 8px; padding-right: 8px; }
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}
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#write input[type="checkbox"] { cursor: pointer; width: inherit; height: inherit; }
figure { overflow-x: auto; margin: 1.2em 0px; max-width: calc(100% + 16px); padding: 0px; }
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.md-math-block:not(:empty)::after { display: none; }
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.md-toc { min-height: 3.58rem; position: relative; font-size: 0.9rem; border-radius: 10px; }
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.md-toc-item { display: block; color: rgb(65, 131, 196); }
.md-toc-item a { text-decoration: none; }
.md-toc-inner:hover { text-decoration: underline; }
.md-toc-inner { display: inline-block; cursor: pointer; }
.md-toc-h1 .md-toc-inner { margin-left: 0px; font-weight: 700; }
.md-toc-h2 .md-toc-inner { margin-left: 2em; }
.md-toc-h3 .md-toc-inner { margin-left: 4em; }
.md-toc-h4 .md-toc-inner { margin-left: 6em; }
.md-toc-h5 .md-toc-inner { margin-left: 8em; }
.md-toc-h6 .md-toc-inner { margin-left: 10em; }
@media screen and (max-width: 48em) {
.md-toc-h3 .md-toc-inner { margin-left: 3.5em; }
.md-toc-h4 .md-toc-inner { margin-left: 5em; }
.md-toc-h5 .md-toc-inner { margin-left: 6.5em; }
.md-toc-h6 .md-toc-inner { margin-left: 8em; }
}
a.md-toc-inner { font-size: inherit; font-style: inherit; font-weight: inherit; line-height: inherit; }
.footnote-line a:not(.reversefootnote) { color: inherit; }
.md-attr { display: none; }
.md-fn-count::after { content: "."; }
code, pre, samp, tt { font-family: var(--monospace); }
kbd { margin: 0px 0.1em; padding: 0.1em 0.6em; font-size: 0.8em; color: rgb(36, 39, 41); background: rgb(255, 255, 255); border: 1px solid rgb(173, 179, 185); border-radius: 3px; box-shadow: rgba(12, 13, 14, 0.2) 0px 1px 0px, rgb(255, 255, 255) 0px 0px 0px 2px inset; white-space: nowrap; vertical-align: middle; }
.md-comment { color: rgb(162, 127, 3); opacity: 0.8; font-family: var(--monospace); }
code { text-align: left; vertical-align: initial; }
a.md-print-anchor { white-space: pre !important; border-width: initial !important; border-style: none !important; border-color: initial !important; display: inline-block !important; position: absolute !important; width: 1px !important; right: 0px !important; outline: 0px !important; background: 0px 0px !important; text-decoration: initial !important; text-shadow: initial !important; }
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.MathJax_SVG_Display, .md-inline-math .MathJax_SVG_Display { width: auto; margin: inherit; display: inline-block !important; }
.MathJax_SVG .MJX-monospace { font-family: var(--monospace); }
.MathJax_SVG .MJX-sans-serif { font-family: sans-serif; }
.MathJax_SVG { display: inline; font-style: normal; font-weight: 400; line-height: normal; zoom: 90%; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; }
.MathJax_SVG * { transition: none 0s ease 0s; }
.MathJax_SVG_Display svg { vertical-align: middle !important; margin-bottom: 0px !important; margin-top: 0px !important; }
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[lang="mermaid"] .node text { font-size: 1rem; }
table tr th { border-bottom: 0px; }
video { max-width: 100%; display: block; margin: 0px auto; }
iframe { max-width: 100%; width: 100%; border: none; }
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.CodeMirror { height: auto; }
.CodeMirror.cm-s-inner { background: inherit; }
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.CodeMirror-gutter-filler, .CodeMirror-scrollbar-filler { background-color: rgb(255, 255, 255); }
.CodeMirror-gutters { border-right: 1px solid rgb(221, 221, 221); background: inherit; white-space: nowrap; }
.CodeMirror-linenumber { padding: 0px 3px 0px 5px; text-align: right; color: rgb(153, 153, 153); }
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<div id='write' class = 'is-node'><p><img src='https://raw.githubusercontent.com/Safe-ly/HACK-UPC-ML/master/logo.png' alt='img' referrerPolicy='no-referrer' /></p><hr /><h1><a name="mckinsey---hack-the-crash" class="md-header-anchor"></a><span>McKinsey - Hack the crash</span></h1><p><span> </span><span>Predicting damage inflicted in traffic accidents.</span></p><h2><a name="data-preprocessing" class="md-header-anchor"></a><span>Data Preprocessing</span></h2><ul><li><p><strong><span>Correlation</span></strong><span> - measured between features and target value allowed sometimes to decide if drop column.</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-keyword">def</span> <span class="cm-def">check_corelation</span>(<span class="cm-variable">csv</span>, <span class="cm-variable">col_1</span>, <span class="cm-variable">col_2</span>):</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">df_corr</span> = <span class="cm-variable">pd</span>.<span class="cm-property">DataFrame</span>()</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">df_corr</span>[<span class="cm-variable">col_1</span>] = <span class="cm-variable">csv</span>[<span class="cm-variable">col_1</span>].<span class="cm-property">astype</span>(<span class="cm-string">'category'</span>).<span class="cm-property">cat</span>.<span class="cm-property">codes</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">df_corr</span>[<span class="cm-variable">col_2</span>] = <span class="cm-variable">csv</span>[<span class="cm-variable">col_2</span>]</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">df_corr</span> = <span class="cm-variable">df_corr</span>.<span class="cm-property">dropna</span>()</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-builtin">print</span>(<span class="cm-variable">df_corr</span>.<span class="cm-property">corr</span>())</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 134px;"></div><div class="CodeMirror-gutters" style="display: none; height: 134px;"></div></div></div></pre></li><li><p><strong><span>Factorization</span></strong></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-keyword">def</span> <span class="cm-def">factorize</span>(<span class="cm-variable">csv</span>, <span class="cm-variable">col_name</span>):</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">dummy</span> = <span class="cm-variable">pd</span>.<span class="cm-property">get_dummies</span>(<span class="cm-variable">csv</span>[<span class="cm-variable">col_name</span>])</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">dummy</span>.<span class="cm-property">columns</span> = [<span class="cm-variable">col_name</span> <span class="cm-operator">+</span> <span class="cm-string">" "</span> <span class="cm-operator">+</span> <span class="cm-builtin">str</span>(<span class="cm-variable">x</span>) <span class="cm-keyword">for</span> <span class="cm-variable">x</span> <span class="cm-keyword">in</span> <span class="cm-variable">dummy</span>.<span class="cm-property">columns</span>]</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">csv</span> = <span class="cm-variable">csv</span>.<span class="cm-property">drop</span>(<span class="cm-variable">col_name</span>, <span class="cm-variable">axis</span>=<span class="cm-number">1</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">csv</span> = <span class="cm-variable">pd</span>.<span class="cm-property">concat</span>([<span class="cm-variable">csv</span>, <span class="cm-variable">dummy</span>], <span class="cm-variable">axis</span>=<span class="cm-number">1</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-keyword">return</span> <span class="cm-variable">csv</span></span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 134px;"></div><div class="CodeMirror-gutters" style="display: none; height: 134px;"></div></div></div></pre></li><li><p><strong><span>Feature Extraction</span></strong><span> - some features were extracted manually</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span> = <span class="cm-variable">pd</span>.<span class="cm-property">DatetimeIndex</span>(<span class="cm-variable">csv</span>[<span class="cm-string">"time"</span>])</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span> = <span class="cm-variable">time</span>.<span class="cm-property">hour</span> <span class="cm-operator">*</span> <span class="cm-number">60</span> <span class="cm-operator">+</span> <span class="cm-variable">time</span>.<span class="cm-property">minute</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span> = <span class="cm-variable">pd</span>.<span class="cm-property">DataFrame</span>(<span class="cm-variable">time</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span>[<span class="cm-variable">time</span> <span class="cm-operator">></span>= <span class="cm-number">1080</span>] = <span class="cm-string">"evening"</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span>[<span class="cm-variable">time</span> <span class="cm-operator">></span>= <span class="cm-number">720</span>] = <span class="cm-string">"midday"</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span>[<span class="cm-variable">time</span> <span class="cm-operator">></span>= <span class="cm-number">360</span>] = <span class="cm-string">"morning"</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">time</span>[<span class="cm-variable">time</span> <span class="cm-operator">></span>= <span class="cm-number">0</span>] = <span class="cm-string">"night"</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">csv</span>[<span class="cm-string">"day_time"</span>] = <span class="cm-variable">time</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">csv</span> = <span class="cm-variable">factorize</span>(<span class="cm-variable">csv</span>, <span class="cm-string">"day_time"</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">csv</span> = <span class="cm-variable">csv</span>.<span class="cm-property">drop</span>(<span class="cm-string">"time"</span>, <span class="cm-variable">axis</span>=<span class="cm-number">1</span>)</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 224px;"></div><div class="CodeMirror-gutters" style="display: none; height: 224px;"></div></div></div></pre></li><li><p><strong><span>Standarization</span></strong></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">logregPipe</span> = <span class="cm-variable">Pipeline</span>([(<span class="cm-string">'scaler'</span>, <span class="cm-variable">StandardScaler</span>()),(<span class="cm-string">'logreg'</span>, <span class="cm-variable">LogisticRegression</span>())])</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">x_train</span>, <span class="cm-variable">y_train</span> = <span class="cm-variable">logregPipe</span>.<span class="cm-property">fit_transform</span>(<span class="cm-variable">x_train</span>, <span class="cm-variable">y_train</span>)</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 45px;"></div><div class="CodeMirror-gutters" style="display: none; height: 45px;"></div></div></div></pre></li></ul><h2><a name="feature-selection" class="md-header-anchor"></a><span>Feature Selection</span></h2><ul><li><p><strong><span>R² Denoisser</span></strong><span> - I've used two regerssors inside denoiser: Decision Tree Regressior and K-Neares Neighbours Regressor</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">R² Score Denoisser</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Regressor: DecisionTreeRegressor</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Number of Selected Features: 71</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 67px;"></div><div class="CodeMirror-gutters" style="display: none; height: 67px;"></div></div></div></pre><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">R² Score Denoisser</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Regressor: KNeighborsRegressor</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Number of Selected Features: 68</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 67px;"></div><div class="CodeMirror-gutters" style="display: none; height: 67px;"></div></div></div></pre></li><li><p><strong><span>Boruta Selection</span></strong><span> - resulted in 16 features.</span></p><p><span>Boruta gives closer look at selected features. The most important were </span><strong><span>Light and Weather Condition</span></strong><span>.</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><span><span></span>x</span></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">BorutaPy finished running.</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text=""></span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Iteration: <span class="cm-tab" role="presentation" cm-text=" "> </span>20 / 100</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Confirmed: <span class="cm-tab" role="presentation" cm-text=" "> </span>16</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Tentative: <span class="cm-tab" role="presentation" cm-text=" "> </span>0</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Rejected: <span class="cm-tab" role="presentation" cm-text=" "> </span>87</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">Number of Selected Features: 16</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 157px;"></div><div class="CodeMirror-gutters" style="display: none; height: 157px;"></div></div></div></pre></li><li><p><strong><span>PCA</span></strong><span> - I've also tried Principal Component Analysis to select the most informative features.</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-keyword">def</span> <span class="cm-def">pca_decomp</span>(<span class="cm-variable">X_train</span>, <span class="cm-variable">Y_train</span>):</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">X_train</span> = <span class="cm-variable">StandardScaler</span>().<span class="cm-property">fit_transform</span>(<span class="cm-variable">X_train</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">pca</span> = <span class="cm-variable">PCA</span>(<span class="cm-variable">n_components</span>=<span class="cm-number">0.9</span>, <span class="cm-variable">svd_solver</span>=<span class="cm-string">'full'</span>, <span class="cm-variable">random_state</span>=<span class="cm-number">0</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">X_train</span> = <span class="cm-variable">pca</span>.<span class="cm-property">fit_transform</span>(<span class="cm-variable">X_train</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">x_train</span>, <span class="cm-variable">x_test</span>, <span class="cm-variable">y_train</span>, <span class="cm-variable">y_test</span> = <span class="cm-variable">train_test_split</span>(<span class="cm-variable">X_train</span>,<span class="cm-variable">Y_train</span>,<span class="cm-variable">test_size</span>=<span class="cm-number">0.3</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-keyword">return</span> (<span class="cm-variable">x_train</span>, <span class="cm-variable">x_test</span>, <span class="cm-variable">y_train</span>, <span class="cm-variable">y_test</span>)</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 134px;"></div><div class="CodeMirror-gutters" style="display: none; height: 134px;"></div></div></div></pre></li></ul><h2><a name="data-analysis" class="md-header-anchor"></a><span>Data Analysis</span></h2><p><span>Plotting distributions also helps to realize if selected features behave properly just before training.</span></p><p><img src='data:image/png;base64,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' alt='img' referrerPolicy='no-referrer' /></p><h2><a name="machine-learning-pipeline" class="md-header-anchor"></a><span>Machine Learning Pipeline</span></h2><p><span>I've used custom pipeline strategy to train and test 5 different ML algorithms. I'll describe it on example of Decision Tree Classifier.</span></p><ul><li><p><strong><span>Pipeline</span></strong><span> - I've used it to perform automated Scaling</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">dectreePipe</span> = <span class="cm-variable">Pipeline</span>([(<span class="cm-string">'scaler'</span>, <span class="cm-variable">StandardScaler</span>()),</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> (<span class="cm-string">'dectree'</span>, <span class="cm-variable">DecisionTreeClassifier</span>())</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> ])</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">dectreeParam</span> = {</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-string">'dectree__criterion'</span>: [<span class="cm-string">'entropy'</span>, <span class="cm-string">'gini'</span>],</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-string">'dectree__class_weight'</span>: [<span class="cm-string">'balanced'</span>, <span class="cm-keyword">None</span>],</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-string">'dectree__max_depth'</span>: <span class="cm-builtin">range</span>(<span class="cm-number">1</span>, <span class="cm-number">7</span>),</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">}</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 179px;"></div><div class="CodeMirror-gutters" style="display: none; height: 179px;"></div></div></div></pre></li><li><p><strong><span>GridSearchCV</span></strong><span> - I've used Grid Search with Cross Validation to select the best model</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" class="cm-tab-wrap-hack" style="padding-right: 0.1px;"><span class="cm-variable">dectreeGS</span> = <span class="cm-variable">GridSearchCV</span>(<span class="cm-variable">dectreePipe</span>, <span class="cm-variable">param_grid</span>=<span class="cm-variable">dectreeParam</span>, <span class="cm-variable">cv</span>=<span class="cm-number">5</span>, <span class="cm-tab" role="presentation" cm-text=" "> </span></span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">scoring</span>=<span class="cm-string">'f1'</span>).<span class="cm-property">fit</span>(<span class="cm-variable">x_train</span>, <span class="cm-variable">y_train</span>)</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">mean_test_score</span> = <span class="cm-variable">dectreeGS</span>.<span class="cm-property">cv_results_</span>[<span class="cm-string">"mean_test_score"</span>]</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">std_test_score</span> = <span class="cm-variable">dectreeGS</span>.<span class="cm-property">cv_results_</span>[<span class="cm-string">"std_test_score"</span>]</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">plt</span>.<span class="cm-property">figure</span>(<span class="cm-variable">figsize</span>=(<span class="cm-number">4</span>, <span class="cm-number">2</span>))</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">plt</span>.<span class="cm-property">errorbar</span>(<span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-variable">mean_test_score</span>.<span class="cm-property">shape</span>[<span class="cm-number">0</span>]), <span class="cm-variable">mean_test_score</span>,</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">std_test_score</span>, <span class="cm-variable">fmt</span>=<span class="cm-string">'ok'</span>)</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 157px;"></div><div class="CodeMirror-gutters" style="display: none; height: 157px;"></div></div></div></pre><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> selected parameter</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">class_weight balanced</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">criterion entropy</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">max_depth 3</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 90px;"></div><div class="CodeMirror-gutters" style="display: none; height: 90px;"></div></div></div></pre><p><img src='data:image/png;base64,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' alt='img' referrerPolicy='no-referrer' /></p></li><li><p><span>Confusion matrix is always a best way to visualize the prediction</span></p><figure><table><thead><tr><th> </th><th><span>Predicted 0</span></th><th><span>Predicted 1</span></th></tr></thead><tbody><tr><td><strong><span>Actual 0</span></strong></td><td><span>14073</span></td><td><span>20092</span></td></tr><tr><td><strong><span>Actual 1</span></strong></td><td><span>2463</span></td><td><span>4652</span></td></tr></tbody></table></figure></li></ul><p><span> </span></p><h2><a name="model-selection" class="md-header-anchor"></a><span>Model Selection</span></h2><ul><li><p><strong><span>Logistic Regression</span></strong><span> - unfortunately GridSearch won't find any parameters that would give satisfiable solution. ROC looks almost flat, we will move forward.</span></p><p><img 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' alt='' referrerPolicy='no-referrer' /></p></li><li><p><strong><span>K-Neighbors Classifier</span></strong><span> - results were better than LogReg but still we need more.</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">knnPipe</span> = <span class="cm-variable">Pipeline</span>([</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> (<span class="cm-string">'scaler'</span>, <span class="cm-variable">StandardScaler</span>()),</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> (<span class="cm-string">'knn'</span>, <span class="cm-variable">KNeighborsClassifier</span>(<span class="cm-variable">n_jobs</span>=<span class="cm-operator">-</span><span class="cm-number">1</span>)),</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">])</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">knnParam</span> = {</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-string">'knn__n_neighbors'</span>: <span class="cm-builtin">range</span>(<span class="cm-number">1</span>, <span class="cm-number">12</span>),</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-string">'knn__weights'</span>: [<span class="cm-string">'uniform'</span>, <span class="cm-string">'distance'</span>],</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">}</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">_</span> = <span class="cm-variable">knnPipe</span>.<span class="cm-property">fit</span>(<span class="cm-variable">x_train</span>, <span class="cm-variable">y_train</span>)</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 202px;"></div><div class="CodeMirror-gutters" style="display: none; height: 202px;"></div></div></div></pre><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">f1_score on the train set: 0.066</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">f1_score on the test set: 0.028</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 45px;"></div><div class="CodeMirror-gutters" style="display: none; height: 45px;"></div></div></div></pre></li><li><p><strong><span>Decision Tree Classifier</span></strong><span> - this one give me the best results . Score below is after training on 10% part of training data, later there will be performed final training.</span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">f1_score on the train set: 0.297</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">f1_score on the test set: 0.292</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 45px;"></div><div class="CodeMirror-gutters" style="display: none; height: 45px;"></div></div></div></pre><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-tab" role="presentation" cm-text=" "> </span><span class="cm-tab" role="presentation" cm-text=" "> </span><span class="cm-tab" role="presentation" cm-text=" "> </span><span class="cm-tab" role="presentation" cm-text=" "> </span>selected parameter</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">class_weight balanced</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">criterion entropy</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">max_depth 3</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 90px;"></div><div class="CodeMirror-gutters" style="display: none; height: 90px;"></div></div></div></pre></li><li><p><strong><span>Random Forest</span></strong><span> - It was very hard to find proper parameters for that algorithm. </span></p><pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-tab" role="presentation" cm-text=" "> </span><span class="cm-tab" role="presentation" cm-text=" "> </span><span class="cm-tab" role="presentation" cm-text=" "> </span><span class="cm-tab" role="presentation" cm-text=" "> </span>selected parameter</span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">bootstrap True</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">max_depth 20</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">max_features auto</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">min_samples_leaf 1</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">min_samples_split 2</span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 134px;"></div><div class="CodeMirror-gutters" style="display: none; height: 134px;"></div></div></div></pre><p><img 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' alt='img' referrerPolicy='no-referrer' /></p></li></ul><p> </p><ul><li><p><strong><span>XGBoost</span></strong><span> - even though it's one of most popular ML algorithm it looses this time with overfitting.</span></p><p><img 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' 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' 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' alt='img' referrerPolicy='no-referrer' /></p></li><li><p><span>Quick Summary of parameters Grid Search.</span></p><ul><li><span>Logistic Regression</span>
<span>f1_score on the train set: 0.0265</span>
<span>f1_score on the test set: 0.0</span></li><li><span>K-Neighbors Classifier</span>
<span>f1_score on the train set: 1.0</span>
<span>f1_score on the test set: 0.102</span></li><li><strong><span>Decision Tree Classifier</span>
<span>f1_score on the train set: 0.291</span>
<span>f1_score on the test set: 0.279</span></strong></li><li><span>RandomForestClassifier</span>
<span>f1_score on the train set 0.969</span>
<span>f1_score on the test set 0.0</span></li><li><span>XGBoost</span>
<span>f1_score on the train set 1.0</span>
<span>f1_score on the test set 0.048</span></li></ul></li></ul><p><span>We choose </span><strong><span>Decision Tree Classifier</span></strong><span> as our algorithm.</span></p><h2><a name="model-selection---part-ii---choosing-features" class="md-header-anchor"></a><span>Model Selection - Part II - choosing features</span></h2><ul><li><strong><span>PCA 68</span></strong>
<span>CV f1 on the train set: 31.3</span>
<span>CV f1 on the test set: 30.8</span></li><li><strong><span>Denoisser KNR 68</span></strong>
<span>CV f1 on the train set: 31.04 </span>
<span>CV f1 on the test set: 30.75 </span></li><li><strong><span>Denoisser DTR 71</span></strong>
<span>CV f1 on the train set: 30.81 </span>
<span>CV f1 on the test set: 31.2 </span></li><li><strong><span>Boruta 16</span></strong>
<span>CV f1 on the train set: 34.59 </span>
<span>CV f1 on the test set: 33.8 </span></li><li><strong><span>All features 103</span></strong>
<span>CV f1 on the train set: 34.19 </span>
<span>CV f1 on the test set: </span><strong><span>34.17</span></strong><span> </span></li></ul><p><span>As a result none of our feature selection technique performed better than whole features set.</span></p><h2><a name="lesson-for-future-projects" class="md-header-anchor"></a><span>Lesson for future projects</span></h2><ul><li><span>Try to use </span><strong><span>PCA</span></strong><span> and other dimension reducing techniques as </span><strong><span>LDA</span></strong><span> or </span><strong><span>QDA</span></strong><span> instead of feature selection.</span></li><li><span>Know your algorithm parameters to properly </span><strong><span>GridSearch</span></strong><span> over them.</span></li><li><span>Feature processing may be very time consuming use functions and generalize your tasks.</span></li></ul><h2><a name="summary" class="md-header-anchor"></a><span>Summary</span></h2><p><span>The best algorithm for predicting damage inflicted in traffic accidents is </span></p><p><code>DecisionTreeClassifier(criterion='entropy', class_weight='balanced', max_depth=5)</code></p><p><span>working on all features and whole set gained F1 Score = </span><strong><span>34.17</span></strong></p><p> </p><p><strong><span>Mateusz Dorobek</span></strong><span> - Team Safely - </span><strong><span>HACK UPC 2019</span></strong></p><hr /><p> </p><p><img src="https://surveymonkey-assets.s3.amazonaws.com/survey/174106945/5405a962-6133-4ae3-8c78-44c9113d9f75.png"></p><hr /><p><img src="https://hackupc.com/assets/img/ogimage.png?v=2019-v2"></p></div>
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