ClassificationTree#
Link to Algorithm description: Decision Tree
- class interpret.glassbox.ClassificationTree(feature_names=None, feature_types=None, max_depth=3, **kwargs)#
Classification 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)#
Fits model to provided instances.
- Parameters:
X – Numpy array for training instances.
y – Numpy array as training labels.
- Returns:
Itself.
- predict(X)#
Predicts on provided instances.
- Parameters:
X – Numpy array for instances.
- Returns:
Predicted class label per instance.
- predict_proba(X)#
Probability estimates on provided instances.
- Parameters:
X – Numpy array for instances.
- Returns:
Probability estimate of instance for each class.
- score(X, y, sample_weight=None)#
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- Parameters:
X (array-like of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns:
score – Mean accuracy of
self.predict(X)w.r.t. y.- Return type:
float