.. _`Decision Tree Classifier`: .. _`org.sysess.sympathy.machinelearning.decision_tree_classifier`: Decision Tree Classifier ~~~~~~~~~~~~~~~~~~~~~~~~ .. image:: tree.svg :width: 48 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. .. versionchanged:: 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. .. versionchanged:: 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. .. versionadded:: 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 :func:`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** : model Model **Definition** *Input ports* *Output ports* :model: model Model .. automodule:: node_DecisionTreeClassifier :noindex: .. class:: DecisionTreeClassifier :noindex: