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Dota 2 problem - HackMD
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<div id="doc" class="markdown-body container-fluid comment-enabled" data-hard-breaks="true" style="position: relative;"><h1 id="Dota-2-problem"><a class="anchor hidden-xs" href="#Dota-2-problem" title="Dota-2-problem"><span class="octicon octicon-link"></span></a>Dota 2 problem</h1><h2 id="Description"><a class="anchor hidden-xs" href="#Description" title="Description"><span class="octicon octicon-link"></span></a>Description</h2><p>In this project I will try to predict the winner of a Dota 2 match based on the information about the first 5 minutes of a game, using two machine learning techniques: Gradient Boosting and Logistic Regression.</p><h3 id="Metric"><a class="anchor hidden-xs" href="#Metric" title="Metric"><span class="octicon octicon-link"></span></a>Metric</h3><p>Metric used to measure prediction quality is AUC-ROC score.</p><h3 id="Target"><a class="anchor hidden-xs" href="#Target" title="Target"><span class="octicon octicon-link"></span></a>Target</h3><p>Target variable is called ‘radiant_win’.</p><h2 id="Gradient-Boosting"><a class="anchor hidden-xs" href="#Gradient-Boosting" title="Gradient-Boosting"><span class="octicon octicon-link"></span></a>Gradient Boosting</h2><h3 id="Preprocessing0"><a class="anchor hidden-xs" href="#Preprocessing0" title="Preprocessing0"><span class="octicon octicon-link"></span></a>Preprocessing</h3><p>The only preprocessing technique used here is filling in the missing values.</p><h4 id="Gaps0"><a class="anchor hidden-xs" href="#Gaps0" title="Gaps0"><span class="octicon octicon-link"></span></a>Gaps</h4><p>Those are the fields, which contain missing values:</p><ul>
<li>‘first_blood_time’</li>
<li>‘first_blood_team’</li>
<li>‘first_blood_player1’ – only one of these will be not NaN</li>
<li>‘first_blood_player2’ – only one of these will be not NaN</li>
<li>‘radiant_bottle_time’</li>
<li>‘radiant_courier_time’</li>
<li>‘radiant_flying_courier_time’</li>
<li>‘radiant_first_ward_time’</li>
<li>‘dire_bottle_time’</li>
<li>‘dire_courier_time’</li>
<li>‘dire_flying_courier_time’</li>
<li>‘dire_first_ward_time’</li>
</ul><p>Most of the gaps are present because the corresponding event hasn’t happened in first 5 minutes. It was decided to replace NaNs with large values.</p><h3 id="Learning0"><a class="anchor hidden-xs" href="#Learning0" title="Learning0"><span class="octicon octicon-link"></span></a>Learning</h3><h4 id="Number-of-trees"><a class="anchor hidden-xs" href="#Number-of-trees" title="Number-of-trees"><span class="octicon octicon-link"></span></a>Number of trees</h4><p>ROC-AUC score and cross-validation time was measured for different <code>n_estimators</code> in gradient boosting:</p><table>
<thead>
<tr>
<th>n_estimators</th>
<th>AUC-ROC</th>
<th>learning time</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0.591</td>
<td>8s</td>
</tr>
<tr>
<td>2</td>
<td>0.610</td>
<td>11s</td>
</tr>
<tr>
<td>3</td>
<td>0.620</td>
<td>14s</td>
</tr>
<tr>
<td>4</td>
<td>0.631</td>
<td>19s</td>
</tr>
<tr>
<td>5</td>
<td>0.640</td>
<td>21s</td>
</tr>
<tr>
<td>10</td>
<td>0.669</td>
<td>37s</td>
</tr>
<tr>
<td>15</td>
<td>0.678</td>
<td>55s</td>
</tr>
<tr>
<td>20</td>
<td>0.683</td>
<td>73s</td>
</tr>
<tr>
<td>30</td>
<td>0.691</td>
<td>107s</td>
</tr>
<tr>
<td>40</td>
<td>0.695</td>
<td>142s</td>
</tr>
</tbody>
</table><p>As it can be seen, the score increases about logarithmically, while the time grows linearly.<br>
Thus, further <code>n_estimators</code> would take too much time with slight gain in accuracy.<br>
For example, according to calculations, it would take 1 hour to achieve 0.8 score.</p><h4 id="Random-subset"><a class="anchor hidden-xs" href="#Random-subset" title="Random-subset"><span class="octicon octicon-link"></span></a>Random subset</h4><p>I have extracted 10% of the initial sample with <code>pd.DataFrame.sample()</code> and used this subset for learning:</p><pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span data-linenumber="1"></span></div><div class="code">features = features_0.sample(frac=<span class="hljs-number">0.1</span>)
</div></div></code></pre><p>With different <code>n_estimators</code> I acheived the following results:</p><table>
<thead>
<tr>
<th>n_estimators</th>
<th>AUC-ROC</th>
<th>learning time</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0.581</td>
<td>0s</td>
</tr>
<tr>
<td>2</td>
<td>0.604</td>
<td>0s</td>
</tr>
<tr>
<td>3</td>
<td>0.633</td>
<td>0s</td>
</tr>
<tr>
<td>4</td>
<td>0.629</td>
<td>1s</td>
</tr>
<tr>
<td>5</td>
<td>0.646</td>
<td>1s</td>
</tr>
<tr>
<td>10</td>
<td>0.658</td>
<td>2s</td>
</tr>
<tr>
<td>15</td>
<td>0.678</td>
<td>2s</td>
</tr>
<tr>
<td>20</td>
<td>0.679</td>
<td>3s</td>
</tr>
<tr>
<td>30</td>
<td>0.684</td>
<td>5s</td>
</tr>
<tr>
<td>40</td>
<td>0.688</td>
<td>7s</td>
</tr>
<tr>
<td>1000</td>
<td>0.686</td>
<td>166s</td>
</tr>
</tbody>
</table><p>Apparently, large values of <code>n_estimators</code> like 1000 result in <em>overfitting</em>. So, in fact we will, most probably, not reach 0.8 score with the initial sample in 1 hour or more. Consequently 30 trees is an optimal amount.<br>
I have also tried using subset sizes different from 10%. There is a pattern: quality on cross-validation decreases slower than time for a fixed amount of trees (if overfitting is not taken into account).</p><h2 id="Logistic-Regression"><a class="anchor hidden-xs" href="#Logistic-Regression" title="Logistic-Regression"><span class="octicon octicon-link"></span></a>Logistic Regression</h2><h3 id="Preprocessing"><a class="anchor hidden-xs" href="#Preprocessing" title="Preprocessing"><span class="octicon octicon-link"></span></a>Preprocessing</h3><h4 id="Gaps"><a class="anchor hidden-xs" href="#Gaps" title="Gaps"><span class="octicon octicon-link"></span></a>Gaps</h4><p>I used the same tactics for gap-filling, except that gaps were filled with zeros rather than 10000 in GradientBoosting.</p><h4 id="Scaling"><a class="anchor hidden-xs" href="#Scaling" title="Scaling"><span class="octicon octicon-link"></span></a>Scaling</h4><p><code>sklearn.preprocessing.StandardScaler</code> was used to scale the data</p><h3 id="Learning"><a class="anchor hidden-xs" href="#Learning" title="Learning"><span class="octicon octicon-link"></span></a>Learning</h3><h5 id="Without-any-preprocessing-of-categorical-features-besides-scaling"><a class="anchor hidden-xs" href="#Without-any-preprocessing-of-categorical-features-besides-scaling" title="Without-any-preprocessing-of-categorical-features-besides-scaling"><span class="octicon octicon-link"></span></a>Without any preprocessing of categorical features besides scaling</h5><table>
<thead>
<tr>
<th>C</th>
<th>AUC-ROC test</th>
<th>AUC-ROC train</th>
<th>learning time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0.001</td>
<td>0.7162</td>
<td>0.7178</td>
<td>5s</td>
</tr>
<tr>
<td>0.01</td>
<td>0.7164</td>
<td>0.7181</td>
<td>8s</td>
</tr>
<tr>
<td>0.1</td>
<td>0.7164</td>
<td>0.7181</td>
<td>8s</td>
</tr>
<tr>
<td>1</td>
<td>0.7163</td>
<td>0.7182</td>
<td>9s</td>
</tr>
<tr>
<td>10</td>
<td>0.7162</td>
<td>0.7182</td>
<td>11s</td>
</tr>
<tr>
<td>100</td>
<td>0.7161</td>
<td>0.7182</td>
<td>13s</td>
</tr>
</tbody>
</table><p>Quality of learning with logistic regression is a little better than with gradient boosting and learning takes less time.</p><h5 id="Without-categorical-features"><a class="anchor hidden-xs" href="#Without-categorical-features" title="Without-categorical-features"><span class="octicon octicon-link"></span></a>Without categorical features</h5><table>
<thead>
<tr>
<th>C</th>
<th>AUC-ROC test</th>
<th>learning time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0.001</td>
<td>0.7175</td>
<td>6s</td>
</tr>
<tr>
<td>0.01</td>
<td>0.7178</td>
<td>8s</td>
</tr>
<tr>
<td>0.1</td>
<td>0.7178</td>
<td>8s</td>
</tr>
<tr>
<td>1</td>
<td>0.7179</td>
<td>9s</td>
</tr>
<tr>
<td>10</td>
<td>0.7179</td>
<td>9s</td>
</tr>
<tr>
<td>100</td>
<td>0.7178</td>
<td>9s</td>
</tr>
</tbody>
</table><p>Quality almost didn’t improve. It is because removed features got weights close to 0 during the first learning, thus they didn’t affect the result much.</p><h5 id="Using-bag-of-words"><a class="anchor hidden-xs" href="#Using-bag-of-words" title="Using-bag-of-words"><span class="octicon octicon-link"></span></a>Using bag-of-words</h5><p>I have replaced rN_hero and dN_hero features with 112 new features, which characterize, whether the hero was picked and by which side he was picked.</p><table>
<thead>
<tr>
<th>C</th>
<th>AUC-ROC test</th>
<th>learning time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0.001</td>
<td>0.7543</td>
<td>11s</td>
</tr>
<tr>
<td>0.01</td>
<td>0.7547</td>
<td>14s</td>
</tr>
<tr>
<td>0.1</td>
<td>0.7547</td>
<td>16s</td>
</tr>
<tr>
<td>1</td>
<td>0.7547</td>
<td>17s</td>
</tr>
<tr>
<td>10</td>
<td>0.7547</td>
<td>19s</td>
</tr>
<tr>
<td>100</td>
<td>0.7547</td>
<td>21s</td>
</tr>
</tbody>
</table><p>Quality increased by 4%. Picking of certain heroes affects the outcome as much as amount of gold, killed units and experience points.<br>
For example, presence of “Queen of Pain” is one of the most important “reasons” of failure of a team.</p><h2 id="Conclusion"><a class="anchor hidden-xs" href="#Conclusion" title="Conclusion"><span class="octicon octicon-link"></span></a>Conclusion</h2><p>The best model is logistic regression with bag-of-words applied to hero names. It shows AUC-ROC score of 0.7547 on cross-validation.<br>
The predictions for <code>radiant_win</code> probability that the model makes vary from 0.010 to 0.995:</p><p><img src="https://i.imgur.com/wx7Z3cB.png" alt=""></p></div>
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var toc = $('.ui-toc');
var tocAffix = $('.ui-affix-toc');
var tocDropdown = $('.ui-toc-dropdown');
//toc
tocDropdown.click(function (e) {
e.stopPropagation();
});
var enoughForAffixToc = true;
function generateScrollspy() {
$(document.body).scrollspy({
target: ''
});
$(document.body).scrollspy('refresh');
if (enoughForAffixToc) {
toc.hide();
tocAffix.show();
} else {
tocAffix.hide();
toc.show();
}
$(document.body).scroll();
}
function windowResize() {
//toc right
var paddingRight = parseFloat(markdown.css('padding-right'));
var right = ($(window).width() - (markdown.offset().left + markdown.outerWidth() - paddingRight));
toc.css('right', right + 'px');
//affix toc left
var newbool;
var rightMargin = (markdown.parent().outerWidth() - markdown.outerWidth()) / 2;
//for ipad or wider device
if (rightMargin >= 133) {
newbool = true;
var affixLeftMargin = (tocAffix.outerWidth() - tocAffix.width()) / 2;
var left = markdown.offset().left + markdown.outerWidth() - affixLeftMargin;
tocAffix.css('left', left + 'px');
} else {
newbool = false;
}
if (newbool != enoughForAffixToc) {
enoughForAffixToc = newbool;
generateScrollspy();
}
}
$(window).resize(function () {
windowResize();
});
$(document).ready(function () {
windowResize();
generateScrollspy();
});
//remove hash
function removeHash() {
window.location.hash = '';
}
var backtotop = $('.back-to-top');
var gotobottom = $('.go-to-bottom');
backtotop.click(function (e) {
e.preventDefault();
e.stopPropagation();
if (scrollToTop)
scrollToTop();
removeHash();
});
gotobottom.click(function (e) {
e.preventDefault();
e.stopPropagation();
if (scrollToBottom)
scrollToBottom();
removeHash();
});
var toggle = $('.expand-toggle');
var tocExpand = false;
checkExpandToggle();
toggle.click(function (e) {
e.preventDefault();
e.stopPropagation();
tocExpand = !tocExpand;
checkExpandToggle();
})
function checkExpandToggle () {
var toc = $('.ui-toc-dropdown .toc');
var toggle = $('.expand-toggle');
if (!tocExpand) {
toc.removeClass('expand');
toggle.text('Expand all');
} else {
toc.addClass('expand');
toggle.text('Collapse all');
}
}
function scrollToTop() {
$('body, html').stop(true, true).animate({
scrollTop: 0
}, 100, "linear");
}
function scrollToBottom() {
$('body, html').stop(true, true).animate({
scrollTop: $(document.body)[0].scrollHeight
}, 100, "linear");
}
</script>
</body>
</html>