D.7. Performance metrics

The most important piece of any machine learning pipeline is the performance metric. If you don’t know how well your machine learning model is working, you can’t make it better. The first thing we do when starting a machine learning pipeline is set up a performance metric, such as “.score()” on any sklearn machine learning model. We then build a completely random classification/regression pipeline with that performance score computed at the end. This lets us make incremental improvements to our pipeline that gradually improve the score, getting us closer to our goal. It’s also a great way to keep your bosses and coworkers convinced that you’re on the right track.

D.7.1. Measuring classifier performance

A classifier ...

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