Beyond Precision and Recall: A Deep Dive Deep into the Tversky Index
<p>In the world of data science, metrics are the compass that guide our models to success. While many are familiar with the classic measures of precision and recall, there are actually a wide range of other options that are worth exploring.</p>
<p>In this article, we’ll dive into the Tversky index. This metric, a generalization of the Dice and Jaccard coefficients, can be extremely useful when trying to balance precision and recall against each other. When implemented as a loss function for neural networks, it can be a powerful way to deal with class imbalances.</p>
<h2>A quick refresher on precision and recall</h2>
<p>Imagine you are a detective tasked with capturing criminals in your town. In truth, there are 10 criminals roaming the streets.</p>
<p>In your first month, you bring in 8 suspects you assume to be criminals. Only 4 of them end up being guilty, while the other 4 are innocent.</p>
<p>If you were a machine learning model, you’d be evaluated against your precision and recall.</p>
<p><strong>Precision</strong> asks: “of all those you caught, how many were criminals?”</p>
<p><strong>Recall</strong> asks: “of all the criminals in the town, how many did you catch?”</p>
<p><a href="https://towardsdatascience.com/beyond-precision-and-recall-a-deep-dive-deep-into-the-tversky-index-2b377c2c30b7"><strong>Read More</strong></a></p>