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How to evaluate a classifier in scikit-learn


In this video, you'll learn how to properly evaluate a classification model using a variety of common tools and metrics, as well as how to adjust the performance of a classifier to best match your business objectives. I'll start by demonstrating the weaknesses of classification accuracy as an evaluation metric. I'll then discuss the confusion matrix, the ROC curve and AUC, and metrics such as sensitivity, specificity, and precision. By the end of the video, you will have a solid foundation for intelligently evaluating your own classification model.

This is the ninth video in the series, Introduction to machine learning with scikit-learn. The notebook and resources shown in the video are available on GitHub.

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