calibrate_classification(ds: dataset, model: model_classification[ds], {
    "param": value,
    ...
}) -> (model_out: model_classification[ds])

Usually employed after the train_classification step to make sure the model’s predicted probabilities are well-calibrated.

Note that currently we only support calibration of already fitted models, which should always be performed on new data not already seen during training. For more information see the scikit-learn documentation.

calibrate_classification(ds: dataset, model: model_classification[ds], {
    "param": value,
    ...
}) -> (model_out: model_classification[ds])
​
target
string
required

Target variable. Name of the column that contains your target values (labels).

​
method
string
default:"isotonic"

Calibration method. Method to use for calibration. isotonic is a non-parametric method that fits a piecewise-constant, strictly increasing function to the predicted probabilities. sigmoid (Platt’s method) is a parametric method that fits a logistic function to the predicted probabilities.

It is not advised to use isotonic calibration with too few calibration samples (much fewer than 1,000) since it tends to overfit.

Values must be one of the following:

  • isotonic
  • sigmoid

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