A decision tree regressor.
Read more in the User Guide.
The function to measure the quality of a split. Supported criteria are “squared_error” 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, “absolute_error” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and “poisson” which uses reduction in the half mean Poisson deviance to find splits.
Added in version 0.18: Mean Absolute Error (MAE) criterion.
Added in version 0.24: Poisson deviance criterion.
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.
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.
For an example of how max_depth influences the model, see Decision Tree Regression.
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.
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.
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.
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.
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.
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.
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.
Added in version 0.19.
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. See Post pruning decision trees with cost complexity pruning for an example of such pruning.
Added in version 0.22.
1: monotonic increase
0: no constraint
-1: monotonic decrease
If monotonic_cst is None, no constraints are applied.
multioutput regressions (i.e. when n_outputs_ > 1),
regressions trained on data with missing values.
Read more in the User Guide.
Added in version 1.4.
feature_importances_ndarray of shape (n_features,)
Return the feature importances.
The inferred value of max_features.
Number of features seen during fit.
Added in version 0.24.
n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
Added in version 1.0.
The number of outputs when fit is performed.
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.
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
[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.
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])
Return the index of the leaf that each sample is predicted as.
Added in version 0.17.
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.
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.
Compute the pruning path during Minimal Cost-Complexity Pruning.
See Minimal Cost-Complexity Pruning for details on the pruning process.
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.
The target values (class labels) as integers or strings.
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.
Bunch
Dictionary-like object, with the following attributes.
Effective alphas of subtree during pruning.
Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.
Return the decision path in the tree.
Added in version 0.18.
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.
Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.
Build a decision tree regressor from the training set (X, y).
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.
The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.
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.
Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.
Fitted estimator.
Return the depth of the decision tree.
The depth of a tree is the maximum distance between the root and any leaf.
The maximum depth of the tree.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
A MetadataRequest encapsulating routing information.
Return the number of leaves of the decision tree.
Number of leaves.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
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.
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.
The predicted classes, or the predict values.
Return coefficient of determination on test data.
The coefficient of determination, \(R^2\), 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.
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.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) w.r.t. 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).
Configure whether metadata should be requested to be passed to the fit method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tofitif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it tofit.
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.Added in version 1.3.
Metadata routing for sample_weight parameter in fit.
The updated object.
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.
Estimator parameters.
Estimator instance.
Configure whether metadata should be requested to be passed to the score method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed toscoreif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it toscore.
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.Added in version 1.3.
Metadata routing for sample_weight parameter in score.
The updated object.
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