Precision and recall

Both precision and recall are related to the four outcomes discussed in the prior lesson and are important evaluation metrics for any machine learning model. An ad CTR model should ideally have high precision (high ROI on ad spend) and recall (relevant audience targeting). Although it is possible to calculate precision and recall by hand, sklearn has some handy implementations that you can easily plug into the existing workflow. In this exercise, you will set up a decision tree and calculate precision and recall.

The pandas module is available as pd in your workspace and the sample DataFrame is loaded as df. The features are loaded in X and the target is loaded in y for use. Additionally, precision_score() and recall_score() from sklearn.metrics are available.

Obtain the training and testing splits for X and y.
Define a decision tree classifier and produce predictions y_pred by fitting the model.
Use implementations from sklearn to get the precision and recall scores.

Exercise

Precision and recall

Instructions