Decision Tree Classifier

Decision Trees (DTs) are a non-parametric supervised learning methodused for classification and regression. The goal is to create a modelthat predicts the value of a target variable by learning simpledecision rules inferred from the data features.

Documentation

Decision Trees (DTs) are a non-parametric supervised learning methodused for classification and regression. The goal is to create a modelthat predicts the value of a target variable by learning simpledecision rules inferred from the data features.

Configuration:

• max_depth

  The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

• criterion

  The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.

• max_features

  The number of features to consider when looking for the best split:

  • If int, then consider max_features features at each split.

  • If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.

  • If “auto”, then max_features=sqrt(n_features).

  • If “sqrt”, then max_features=sqrt(n_features).

  • If “log2”, then max_features=log2(n_features).

  • If None, then max_features=n_features.

  Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

• min_samples_split

  The minimum number of samples required to split an internal node:

  • If int, then consider min_samples_split as the minimum number.

  • If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  Changed in version 0.18: Added float values for fractions.

• min_samples_leaf

  The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

  • If int, then consider min_samples_leaf as the minimum number.

  • If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  Changed in version 0.18: Added float values for fractions.

• max_leaf_nodes

  Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• min_weight_fraction_leaf

  The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

• splitter

  The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.

• min_impurity_decrease

  A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  The weighted impurity decrease equation is the following:

  N_t / N * (impurity - N_t_R / N_t * right_impurity
                      - N_t_L / N_t * left_impurity)
  

  where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

  N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

  New in version 0.19.

• random_state

  Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer. See random_state for details.

Attributes:

• classes_

  The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

• feature_importances_

  The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance _.

  Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance() as an alternative.

• max_features_

  The inferred value of max_features.

• n_classes_

  The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).

• n_features_

  The number of features when fit is performed.

• n_outputs_

  The number of outputs when fit is performed.

Input ports:

Output ports:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_DecisionTreeClassifier.DecisionTreeClassifier