# APLRRegressor#

Link to Algorithm description: Automatic Piecewise Linear Regression

The below API reference explains the methods added by the light wrapper in InterpretML. The API reference for the remaining members and methods can be found here.

class interpret.glassbox.APLRRegressor(**kwargs)#

Initializes class.

Parameters:

**kwargs – Kwargs pass to APLRRegressor at initialization time.

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, visualizing feature-value pairs for each instance as horizontal bar charts.

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).