API_REFERENCE_FOR_APLR_TUNER.md

APLRTuner

class aplr.APLRTuner(parameters: Union[Dict[str, List[float]], List[Dict[str, List[float]]]] = {"max_interaction_level": [0, 1], "min_observations_in_split": [4, 10, 20, 100, 500, 1000]}, is_regressor: bool = True)

Constructor parameters

parameters (default = {"max_interaction_level": [0, 1], "min_observations_in_split": [4, 10, 20, 100, 500, 1000]})

The parameters that you wish to tune.

is_regressor (default = True)

Whether you want to use APLRRegressor (True) or APLRClassifier (False).

Method: fit(X: FloatMatrix, y: FloatVector, **kwargs)

This method tunes the model to data.

Parameters

X

A numpy matrix with predictor values.

y

A numpy vector with response values.

kwargs

Optional parameters sent to the fit methods in the underlying APLRRegressor or APLRClassifier models.

Method: predict(X: FloatMatrix, **kwargs)

Returns the predictions of the best tuned model as a numpy array if regression or as a list of strings if classification.

Parameters

X

A numpy matrix with predictor values.

kwargs

Optional parameters sent to the predict method in the best tuned model.

Method: predict_class_probabilities(X: FloatMatrix, **kwargs)

This method returns predicted class probabilities of the best tuned model as a numpy matrix.

Parameters

X

A numpy matrix with predictor values.

kwargs

Optional parameters sent to the predict_class_probabilities method in the best tuned model.

Method: predict_proba(X: FloatMatrix, **kwargs)

This method returns predicted class probabilities of the best tuned model as a numpy matrix. Similar to the predict_class_probabilities method but the name predict_proba is compatible with scikit-learn.

Parameters

X

A numpy matrix with predictor values.

kwargs

Optional parameters sent to the predict_class_probabilities method in the best tuned model.

Method: get_best_estimator()

Returns the best tuned model. This is an APLRRegressor or APLRClassifier object.

Method: get_cv_results()

Returns the cv results from the tuning as a list of dictionaries, List[Dict[str, float]].