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Welcome to this short introduction to Precision, Recall and F1. You might have come across these terms when reading about classification models and machine learning, but basically, theyre all ways to measure the accuracy of a model. When you build a model to predict a certain class or category, you need a way to measure how accurate the predictions are. This is what precision, recall and F1 do. They measure the classification models accuracy. In our video on the confusion matrix, we learned about true positives and negatives, and false positives and negatives. This is how many times a model correctly or incorrectly predicts a class. Precision, recall and F1 use these to measure a model as making many mistakes, when predicting class, or if its doing a pretty good job at being spot on in its predictions. But precision, recall and F1 measure different things. so lets break it down into each of their parts and the role each play in measuring a models accuracy. Lets say your
