# Difference between revisions of "sklearn.tree.DecisionTreeRegressor"

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* http://scikit-learn.org/stable/modules/tree.html#regression | * http://scikit-learn.org/stable/modules/tree.html#regression | ||

− | ** [[decision tree training system|Decision trees]] can also be applied to [[regression problem]]s, using the [[DecisionTreeRegressor class]]. <P> As in the [[supervised classification task|classification setting]], the fit method will take as argument arrays X and y, only that in this case y is expected to have [[floating point value]]s instead of [[integer value]]s: | + | ** [[decision tree training system|Decision trees]] can also be applied to [[regression problem]]s, using the [[sklearn.tree.DecisionTreeRegressor|DecisionTreeRegressor class]]. <P> As in the [[supervised classification task|classification setting]], the fit method will take as argument arrays X and y, only that in this case y is expected to have [[floating point value]]s instead of [[integer value]]s: |

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[[Category:Concept]] | [[Category:Concept]] |

## Revision as of 20:45, 23 December 2019

A sklearn.tree.DecisionTreeRegressor is a regression tree learning system within `sklearn.tree`

.

**AKA:**tree.DecisionTreeRegressor, DecisionTreeRegressor**Context**- Usage:

- 1) Import Regression Tree Learning System from scikit-learn :
`from sklearn.tree import DecisionTreeRegressor`

- 2) Create design matrix
`X`

and response vector`Y`

- 3) Create Decision Tree Regressor object:
`DTreg=DecisionTreeRegressor(criterion=’mse’, splitter=’best’[, max_depth=None, min_samples_split=2, min_samples_leaf=1,...])`

- 4) Choose method(s):
`DTreg`

.`apply(X[, check_input])`

, returns the leaf index for each sample predictor.`DTreg`

.`decision_path(X[, check_input])`

, returns the decision path in the tree.`DTreg`

.`fit(X, y[, sample_weight, check_input,...])`

builds a decision tree regressor from the training set (X, y).`DTreg`

.`get_params([deep])`

returns parameters for this estimator.`DTreg`

.`predict(X[, check_input])`

, predicts regression value for X.`DTreg`

.`score(X, y[, sample_weight])`

, returns the coefficient of determination R^2 of the prediction.`DTreg`

.`set_params(**params)`

, sets the parameters of this estimator.

- 1) Import Regression Tree Learning System from scikit-learn :

**Example(s):****Counter-Example(s):****See:**Decision Tree, Regression System, Regularization Task, Ridge Regression Task, Kernel-based Classification Algorithm.

## References

### 2017

- (Scikit-Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html Retrieved:2017-10-22
- QUOTE:
`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, presort=False)`

Read more in the User Guide.

- QUOTE:

### 2015

- http://scikit-learn.org/stable/modules/tree.html#regression
- Decision trees can also be applied to regression problems, using the DecisionTreeRegressor class.
As in the classification setting, the fit method will take as argument arrays X and y, only that in this case y is expected to have floating point values instead of integer values:

- Decision trees can also be applied to regression problems, using the DecisionTreeRegressor class.