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Exercise

Precision-Recall trade-off

When working with classification tasks, the term Precision-Recall trade-off often appears. Where does it comes from?

Usually, the class with higher probability (obtained by the .predict_proba() method) is chosen to assign the document to. But, what if the maximum probability is equal to 0.1? Should you consider that document to belong to this class with only 10% probability?

The answer varies according to problem at hand. It is possible to add a minimum threshold to accept the classification, and by changing the threshold the values of precision and recall move in opposite directions.

The variables y_true and the model model are loaded. Also, if the probability is lower than the threshold, the document will be assigned to DEFAULT_CLASS (chosen to be class 2).

Instructions
100 XP
  • Use the .predict_proba() method to get the probabilities for each class and store them in the pred_probabilities variable.
  • Accept the maximum probability only if it is greater than or equal to 0.5 and store the results in the y_pred_50 variable.
  • Use the np.argmax() and np.max() functions to do the same for a threshold equal to 0.8.
  • Print the trade_off variable with all the metrics.
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