DashAI.back.models.DecisionTreeClassifier

class DecisionTreeClassifier(**kwargs)

Scikit-learn’s Decision Tree Classifier wrapper for DashAI.

__init__(**kwargs) → None

Methods

__init__(**kwargs)
apply(X[, check_input])	Return the index of the leaf that each sample is predicted as.
cost_complexity_pruning_path(X, y[, ...])	Compute the pruning path during Minimal Cost-Complexity Pruning.
decision_path(X[, check_input])	Return the decision path in the tree.
fit(x_train, y_train)	Fit the estimator.
get_depth()	Return the depth of the decision tree.
get_metadata_routing()	Get metadata routing of this object.
get_n_leaves()	Return the number of leaves of the decision tree.
get_params([deep])	Get parameters for this estimator.
get_schema()	Generates the component related Json Schema.
load(filename)	Load the model of the specified path.
predict(x_pred)	Make a prediction with the model.
predict_log_proba(X)	Predict class log-probabilities of the input samples X.
predict_proba(X[, check_input])	Predict class probabilities of the input samples X.
save(filename)	Save the model in the specified path.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_fit_request(*[, x_train, y_train])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_predict_proba_request(*[, check_input])	Request metadata passed to the predict_proba method.
set_predict_request(*[, x_pred])	Request metadata passed to the predict method.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.
validate_and_transform(raw_data)	It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Attributes

COMPATIBLE_COMPONENTS
TYPE
feature_importances_	Return the feature importances.