How to Avoid Being Fooled by Model Accuracy

<h1>Background &mdash; Simple on the Surface</h1> <p>The metrics used for gauging performance of classification models are fairly straightforward, at least from a mathematical standpoint. Nevertheless, I have observed that many modellers and data scientists encounter difficulty articulating these metrics, and even apply them incorrectly. This is an easy mistake to make, as these metrics appear simple on the surface, yet their implications can be profound depending on the problem domain.</p> <p>This article serves as a visual guide to explaining common classification model metrics. We will explore definitions and use examples to highlight where metrics are used inappropriately.</p> <h2>A Brief Note on Visualisation</h2> <p>Each visualisation comprises of ninety subjects, representing anything we might wish to classify. Blue subjects denote negative samples, whilst red are positive samples. The purple box is the model which attempts to predict positive samples. Anything inside this box is what the model predicts as positive.</p> <p><a href="https://towardsdatascience.com/how-to-avoid-being-fooled-by-model-accuracy-e26307385fe1"><strong>Click Here</strong></a></p>