sklearn.tree.DecisionTreeRegressor  
class sklearn.tree.DecisionTreeRegressor(*, criterion='mse', 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, min_impurity_split=None, ccp_alpha=0.0) [source]
 
A decision tree regressor. Read more in the User Guide.  Parameters 
 
criterion{“mse”, “friedman_mse”, “mae”, “poisson”}, default=”mse” 

The function to measure the quality of a split. Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, “friedman_mse”, which uses mean squared error with Friedman’s improvement score for potential splits, “mae” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and “poisson” which uses reduction in Poisson deviance to find splits.  New in version 0.18: Mean Absolute Error (MAE) criterion.   New in version 0.24: Poisson deviance criterion.   
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_depthint, 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_splitint 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_leafint 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_leaffloat, 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_featuresint, 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 int(max_features * n_features) features are considered at each split. If “auto”, then max_features=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.  
random_stateint, 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_nodesint, 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_decreasefloat, 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.   
min_impurity_splitfloat, default=0 

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. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Use min_impurity_decrease instead.   
ccp_alphanon-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 
 
feature_importances_ndarray of shape (n_features,) 

Return the feature importances.  
max_features_int 

The inferred value of max_features.  
n_features_int 

The number of features when fit is performed.  
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  
DecisionTreeClassifier


A decision tree classifier.    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. 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_diabetes
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = DecisionTreeRegressor(random_state=0)
>>> cross_val_score(regressor, X, y, cv=10)
...                    
...
array([-0.39..., -0.46...,  0.02...,  0.06..., -0.50...,
       0.16...,  0.11..., -0.73..., -0.30..., -0.00...])
 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 regressor from the training set (X, y).  
get_depth() Return the depth of the decision tree.  
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.  
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.  
set_params(**params) Set the parameters of this estimator.    
apply(X, check_input=True) [source]
 
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_inputbool, default=True 

Allow to bypass several input checking. Don’t use this parameter unless you know what you do.    Returns 
 
X_leavesarray-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) [source]
 
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.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

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

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) [source]
 
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_inputbool, default=True 

Allow to bypass several input checking. Don’t use this parameter unless you know what you do.    Returns 
 
indicatorsparse 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, X_idx_sorted='deprecated') [source]
 
Build a decision tree regressor 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.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.  
sample_weightarray-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.  
check_inputbool, default=True 

Allow to bypass several input checking. Don’t use this parameter unless you know what you do.  
X_idx_sorteddeprecated, default=”deprecated” 

This parameter is deprecated and has no effect. It will be removed in 1.1 (renaming of 0.26).  Deprecated since version 0.24.     Returns 
 
selfDecisionTreeRegressor 

Fitted estimator.     
  
get_depth() [source]
 
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_depthint 

The maximum depth of the tree.     
  
get_n_leaves() [source]
 
Return the number of leaves of the decision tree.  Returns 
 
self.tree_.n_leavesint 

Number of leaves.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

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

Parameter names mapped to their values.     
  
predict(X, check_input=True) [source]
 
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_inputbool, default=True 

Allow to bypass several input checking. Don’t use this parameter unless you know what you do.    Returns 
 
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

The predicted classes, or the predict values.     
  
score(X, y, sample_weight=None) [source]
 
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True values for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

\(R^2\) of self.predict(X) wrt. y.     Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). 
  
set_params(**params) [source]
 
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 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
 
 Examples using sklearn.tree.DecisionTreeRegressor
 
  Release Highlights for scikit-learn 0.24  

  Release Highlights for scikit-learn 0.22  

  Decision Tree Regression  

  Multi-output Decision Tree Regression  

  Decision Tree Regression with AdaBoost  

  Single estimator versus bagging: bias-variance decomposition  

  Advanced Plotting With Partial Dependence  

  Imputing missing values with variants of IterativeImputer  

  Using KBinsDiscretizer to discretize continuous features