Load the MNIST dataset:

Train a random forest classifier without any recalibration:

Visualize the calibration curve on a test set:

Train a random forest classifier with recalibration:

Visualize the calibration curve on a test set:

Train a classifier:

Compute the class probabilities of a new example:

Check if the model has been calibrated:

Temporarily set a recalibration function to apply to the probabilities:

Set a permanent recalibration function to apply to the probabilities:

Compute the class probabilities of a new example:

Remove the recalibration function from the classifier:

Load the Boston Homes dataset:

Train a predictor with model calibration:

Visualize the comparison plot on a test set:

Remove the recalibration function from the predictor:

Visualize the new comparison plot:

Train a predictor:

Compute the prediction:

Compute the predictive distribution:

Temporarily set a recalibration function to apply to the prediction:

Compute the predictive distribution with this new recalibration:

Load the Titanic dataset:

Train a nearest neighbors classifier with no calibration function:

The classifier is slightly overconfident:

Select the worst classification case in the test set:

Evaluate the estimated probabilities:

Use "temperature scaling" to reduce the classifier self-confidence: