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.