# 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_depthThe 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.

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

max_featuresThe 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_splitThe 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_leafThe 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_nodesGrow 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_leafThe 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.

splitterThe 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_decreaseA 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_stateControls 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*:**model**modelModel

**Definition**

*Input ports*

*Output ports*

- model
model

Model