BayesTreePowerCurve

Bayesian Additive Regressions Trees (BART) based power curve model.

from dwse import BayesTreePowerCurve
model = BayesTreePowerCurve()
model.fit(X_train, y_train)
prediction = model.fit(X_test)

class dswe.bayes_tree.BayesTreePowerCurve(n_trees=200)[source]

Parameters: n_trees (int) – Number of trees to use. An integer greater than 0.

fit(X_train, y_train)[source]

Parameters

X_train (np.ndarray or pd.DataFrame) – A matrix or dataframe of input variable values in the training dataset.
y_train (np.array) – A numeric array for response values in the training dataset.

Returns

self with trained parameter values.

Return type

BayesTreePowerCurve

predict(X_test)[source]

Parameters: X_test (np.ndarray or pd.DataFrame) – A matrix or dataframe of test input variable values to compute predictions.
Returns: A numeric array for predictions at the data points in X_test.
Return type: np.array