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# `sklearn.tree`.DecisionTreeClassifier

class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)

  A decision tree classifier.

  Read more in the User Guide.

  Parameters:
    criterion{“gini”, “entropy”, “log_loss”}, default=”gini”

      The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical formulation.

    splitter{“best”, “random”}, default=”best”

      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_depth int, default=None

      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.

    min_samples_split int or float, default=2

      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 int or float, default=1

      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.

    min_weight_fraction_leaf float, default=0.0

      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.

    max_features int, float or {“auto”, “sqrt”, “log2”}, default=None

      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 `max(1, int(max_features * n_features_in_))` features are considered at each split.

      > - 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.

    random_state int, RandomState instance or None, default=None

      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 Glossary for details.

    max_leaf_nodes int, default=None

      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_decrease float, default=0.0

      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.

    class_weight dict, list of dict or “balanced”, default=None

      Weights associated with classes in the form `{class_label: weight}`. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

      Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

      The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes * np.bincount(y))`

      For multi-output, the weights of each column of y will be multiplied.

      Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

    ccp_alpha non-negative float, default=0.0

      Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than `ccp_alpha` will be chosen. By default, no pruning is performed. See Minimal Cost-Complexity Pruning for details.

      New in version 0.22.

  Attributes:
    classes_ndarray of shape (n_classes,) or list of ndarray

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

    `feature_importances_`ndarray of shape (n_features,)

      Return the feature importances.

    max_features_int

      The inferred value of max_features.

    n_classes_int or list of int

      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_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    n_outputs_int

      The number of outputs when `fit` is performed.

    tree_Tree instance

      The underlying Tree object. Please refer to `help(sklearn.tree._tree.Tree)` for attributes of Tree object and Understanding the decision tree structure for basic usage of these attributes.

  See also

  `DecisionTreeRegressor`

    A decision tree regressor.

  Notes

  The default values for the parameters controlling the size of the trees (e.g. `max_depth`, `min_samples_leaf`, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

  The `predict` method operates using the `numpy.argmax` function on the outputs of `predict_proba`. This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in classes_.

  References

  [1]

  https://en.wikipedia.org/wiki/Decision_tree_learning

  [2]

  L. Breiman, J. Friedman, R. Olshen, and C. Stone, “Classification and Regression Trees”, Wadsworth, Belmont, CA, 1984.

  [3]

  T. Hastie, R. Tibshirani and J. Friedman. “Elements of Statistical Learning”, Springer, 2009.

  [4]

  L. Breiman, and A. Cutler, “Random Forests”, https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

  Examples

      >>> from sklearn.datasets import load_iris
      >>> from sklearn.model_selection import cross_val_score
      >>> from sklearn.tree import DecisionTreeClassifier
      >>> clf = DecisionTreeClassifier(random_state=0)
      >>> iris = load_iris()
      >>> cross_val_score(clf, iris.data, iris.target, cv=10)
      ...
      ...
      array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,
              0.93...,  0.93...,  1.     ,  0.93...,  1.      ])

  Methods

  `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, y[, sample_weight, check_input]) | Build a decision tree classifier from the training set (X, y).
  `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.
  `predict`(X[, check_input]) | Predict class or regression value for X.
  `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.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_fit_request`(*[, check_input, sample_weight]) | 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`(*[, check_input]) | Request metadata passed to the `predict` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  apply(X, check_input=True)

    Return the index of the leaf that each sample is predicted as.

    New in version 0.17.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

      check_input bool, default=True

        Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

    Returns:
      X_leaves array-like of shape (n_samples,)

        For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within `[0; self.tree_.node_count)`, possibly with gaps in the numbering.

  cost_complexity_pruning_path(X, y, sample_weight=None)

    Compute the pruning path during Minimal Cost-Complexity Pruning.

    See Minimal Cost-Complexity Pruning for details on the pruning process.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The training input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc_matrix`.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        The target values (class labels) as integers or strings.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

    Returns:
      ccp_path `Bunch`

        Dictionary-like object, with the following attributes.

        ccp_alphasndarray

          Effective alphas of subtree during pruning.

        impuritiesndarray

          Sum of the impurities of the subtree leaves for the corresponding alpha value in `ccp_alphas`.

  decision_path(X, check_input=True)

    Return the decision path in the tree.

    New in version 0.18.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

      check_input bool, default=True

        Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

    Returns:
      indicator sparse matrix of shape (n_samples, n_nodes)

        Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

  property feature_importances_

    Return the feature importances.

    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.

    Returns:
      feature_importances_ndarray of shape (n_features,)

        Normalized total reduction of criteria by feature (Gini importance).

  fit(X, y, sample_weight=None, check_input=True)

    Build a decision tree classifier from the training set (X, y).

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The training input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc_matrix`.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        The target values (class labels) as integers or strings.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.

      check_input bool, default=True

        Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

    Returns:
      self DecisionTreeClassifier

        Fitted estimator.

  get_depth()

    Return the depth of the decision tree.

    The depth of a tree is the maximum distance between the root and any leaf.

    Returns:
      self.tree_.max_depth int

        The maximum depth of the tree.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_n_leaves()

    Return the number of leaves of the decision tree.

    Returns:
      self.tree_.n_leaves int

        Number of leaves.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X, check_input=True)

    Predict class or regression value for X.

    For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

      check_input bool, default=True

        Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

    Returns:
      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        The predicted classes, or the predict values.

  predict_log_proba(X)

    Predict class log-probabilities of the input samples X.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

    Returns:
      proba ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1

        The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

  predict_proba(X, check_input=True)

    Predict class probabilities of the input samples X.

    The predicted class probability is the fraction of samples of the same class in a leaf.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

      check_input bool, default=True

        Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

    Returns:
      proba ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1

        The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

  score(X, y, sample_weight=None)

    Return the mean accuracy on the given test data and labels.

    In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        Mean accuracy of `self.predict(X)` w.r.t. `y`.

  set_fit_request(*, check_input: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      check_input str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `check_input` parameter in `fit`.

      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_predict_proba_request(*, check_input: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

    Request metadata passed to the `predict_proba` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `predict_proba` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `predict_proba`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      check_input str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `check_input` parameter in `predict_proba`.

    Returns:
      self object

        The updated object.

  set_predict_request(*, check_input: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

    Request metadata passed to the `predict` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `predict` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `predict`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      check_input str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `check_input` parameter in `predict`.

    Returns:
      self object

        The updated object.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DecisionTreeClassifier

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.tree.DecisionTreeClassifier`

Release Highlights for scikit-learn 1.3

Release Highlights for scikit-learn 1.3

Classifier comparison

Classifier comparison

Plot the decision surface of decision trees trained on the iris dataset

Plot the decision surface of decision trees trained on the iris dataset

Post pruning decision trees with cost complexity pruning

Post pruning decision trees with cost complexity pruning

Understanding the decision tree structure

Understanding the decision tree structure

Discrete versus Real AdaBoost

Discrete versus Real AdaBoost

Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

Plot the decision boundaries of a VotingClassifier

Plot the decision boundaries of a VotingClassifier

Plot the decision surfaces of ensembles of trees on the iris dataset

Plot the decision surfaces of ensembles of trees on the iris dataset

Two-class AdaBoost

Two-class AdaBoost

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV