experiments.baselines.decision_tree.DecisionTreeClassifierBaseline

class DecisionTreeClassifierBaseline(**dt_kwargs)

Bases: SupervisedExperimentBaseline

__init__(**dt_kwargs)

Implements a decision tree classifier baseline for a binary classification task. Pass any keyword arguments to this class that scikit-learn’s DecisionTreeClassifier constructor takes.

__repr__()

Return repr(self).

Methods

predict(theta, X)

Use the trained model to predict positive class probabilities. theta isn’t used here because there are no fitted parameters in decision trees.

Parameters:

• theta – Model weights, None in this case

• X (2D np.ndarray) – features

train(X, y)

Instantiate a new model instance and train (fit) it to the training data, X,y

Parameters:

• X (2D np.ndarray) – features

• y (1D np.ndarray) – labels