Decision Tree Classifier

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_DecisionTreeClassifier.DecisionTreeClassifier

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.

• 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.

• 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 percentage 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 percentage 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 percentages.

• min_samples_leaf

  The minimum number of samples required to be at a leaf node:

  • If int, then consider min_samples_leaf as the minimum number.
  • If float, then min_samples_leaf is a percentage 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 percentages.

• 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_impurity_split

  Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

  Deprecated since version 0.19: min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19 and will be removed in 0.21. Use min_impurity_decrease instead.

• 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.

• presort

  Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training.

• random_state

  If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

• classes_

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

• feature_importances_

  The 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 _.

• 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.

Inputs: Outputs:

model : model

Model