RegressionTree#

Link to Algorithm description: Decision Tree

class interpret.glassbox.RegressionTree(feature_names=None, feature_types=None, max_depth=3, **kwargs)#

Regression tree with shallow depth.

Initializes tree with low depth.

Parameters:

  • feature_names – List of feature names.

  • feature_types – List of feature types.

  • max_depth – Max depth of tree.

  • **kwargs – Kwargs sent to __init__() method of tree.

explain_global(name=None)#

Provides global explanation for model.

Parameters:

name – User-defined explanation name.

Returns:

An explanation object, visualizing feature-value pairs as horizontal bar chart.

explain_local(X, y=None, name=None)#

Provides local explanations for provided instances.

Parameters:

  • X – Numpy array for X to explain.

  • y – Numpy vector for y to explain.

  • name – User-defined explanation name.

Returns:

An explanation object.

fit(X, y, sample_weight=None, check_input=True)#

Fits model to provided instances.

Parameters:

  • X – Numpy array for training instances.

  • y – Numpy array as training labels.

  • sample_weight (optional[np.ndarray]) – (n_samples,) Sample weights. If None (default), then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.

  • check_input (bool) – default=True. Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

Returns:

Itself.

predict(X)#

Predicts on provided instances.

Parameters:

X – Numpy array for instances.

Returns:

Predicted class label per instance.

score(X, y, sample_weight=None)#

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).