<p>There is a technique where you can write you own loss function to focus on the ranking metrics like(AUC, Precision-Recall) rather than the classification losses like(hinge loss or log-loss).</p>

<p>Refer section 4( Maximizing Recall at Fixed Precision) of the paper
Scalable Learning of Non-Decomposable Objectives (<a href="https://arxiv.org/pdf/1608.04802.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1608.04802.pdf</a>) for more details.</p>
How can the model be tuned to improve precision, when precision is much more important than recall?
<p>The performance of a machine learning classifier can be measured by a variety of metrics like precision, recall, and classification accuracy, among other metrics.</p>

<p>Given code like this:</p>

<pre><code>clf = svm.SVC(kernel='rbf')
clf.fit(X_train, y_train)
</code></pre>

<ol>
<li><p>What metric is the fit function trying to optimze?</p></li>
<li><p>How can the model be tuned to improve precision, when precision is much more important than recall?</p></li>
</ol>