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Docs: minor fixes

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Alinson S. Xavier
2020-05-05 13:41:12 -05:00
parent c15ec9e2c8
commit 929ff0d9d8
10 changed files with 17 additions and 17 deletions
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docs/customization/index.html
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@@ -142,7 +142,7 @@
<li class="second-level"><a href="#selecting-solver-components">Selecting solver components</a></li>
<li class="second-level"><a href="#adjusting-component-aggresiveness">Adjusting component aggresiveness</a></li>
<li class="second-level"><a href="#adjusting-component-aggressiveness">Adjusting component aggressiveness</a></li>
<li class="second-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
@@ -189,11 +189,11 @@ solver = LearningSolver()
solver.add(LazyConstraintComponent(...))
</code></pre>
<h2 id="adjusting-component-aggresiveness">Adjusting component aggresiveness</h2>
<h2 id="adjusting-component-aggressiveness">Adjusting component aggressiveness</h2>
<p>The aggressiveness of classification components (such as <code>PrimalSolutionComponent</code> and <code>LazyConstraintComponent</code>) can
be adjusted through the <code>threshold</code> constructor argument. Internally, these components ask the ML models how confident
they are on each prediction (through the <code>predict_proba</code> method in the sklearn API), and only take into account
predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggresiveness,
predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggressiveness,
while raising a component's threshold makes it more conservative. </p>
<p>MIPLearn also includes <code>MinPrecisionThreshold</code>, a dynamic threshold which adjusts itself automatically during training
to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize