- class sklearn.tree.DecisionTreeRegressor(criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_density=0.1, max_features=None, compute_importances=False, random_state=None)¶
A tree regressor.
criterion : string, optional (default=”mse”)
The function to measure the quality of a split. The only supported criterion is “mse” for the mean squared error.
max_features : int, string or None, optional (default=None)
- The number of features to consider when looking for the best split:
- If “auto”, then max_features=sqrt(n_features) on classification tasks and max_features=n_features on regression problems.
- If “sqrt”, then max_features=sqrt(n_features).
- If “log2”, then max_features=log2(n_features).
- If None, then max_features=n_features.
max_depth : integer or None, optional (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 : integer, optional (default=2)
The minimum number of samples required to split an internal node.
min_samples_leaf : integer, optional (default=1)
The minimum number of samples required to be at a leaf node.
min_density : float, optional (default=0.1)
This parameter controls a trade-off in an optimization heuristic. It controls the minimum density of the sample_mask (i.e. the fraction of samples in the mask). If the density falls below this threshold the mask is recomputed and the input data is packed which results in data copying. If min_density equals to one, the partitions are always represented as copies of the original data. Otherwise, partitions are represented as bit masks (aka sample masks).
compute_importances : boolean, optional (default=True)
Whether feature importances are computed and stored into the feature_importances_ attribute when calling fit.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
[R119] http://en.wikipedia.org/wiki/Decision_tree_learning [R120] L. Breiman, J. Friedman, R. Olshen, and C. Stone, “Classification and Regression Trees”, Wadsworth, Belmont, CA, 1984. [R121] T. Hastie, R. Tibshirani and J. Friedman. “Elements of Statistical Learning”, Springer, 2009. [R122] (1, 2) L. Breiman, and A. Cutler, “Random Forests”, http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
>>> from sklearn.datasets import load_boston >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeRegressor
>>> boston = load_boston() >>> regressor = DecisionTreeRegressor(random_state=0)
R2 scores (a.k.a. coefficient of determination) over 10-folds CV:
>>> cross_val_score(regressor, boston.data, boston.target, cv=10) ... ... array([ 0.61..., 0.57..., -0.34..., 0.41..., 0.75..., 0.07..., 0.29..., 0.33..., -1.42..., -1.77...])
tree_ Tree object The underlying Tree object. feature_importances_ array of shape = [n_features] The feature importances (the higher, the more important the feature). The importance of a feature is computed as the (normalized) total reduction of error brought by that feature. It is also known as the Gini importance [R122].
fit(X, y[, sample_mask, X_argsorted, ...]) Build a decision tree from the training set (X, y). fit_transform(X[, y]) Fit to data, then transform it get_params([deep]) Get parameters for the estimator predict(X) Predict class or regression value for X. score(X, y) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of the estimator. transform(X[, threshold]) Reduce X to its most important features.
- __init__(criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_density=0.1, max_features=None, compute_importances=False, random_state=None)¶
- fit(X, y, sample_mask=None, X_argsorted=None, check_input=True, sample_weight=None)¶
Build a decision tree from the training set (X, y).
X : array-like, shape = [n_samples, n_features]
The training input samples. Use dtype=np.float32 and order='F' for maximum efficiency.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (integers that correspond to classes in classification, real numbers in regression). Use dtype=np.float64 and order='C' for maximum efficiency.
sample_mask : array-like, shape = [n_samples], dtype = bool or None
A bit mask that encodes the rows of X that should be used to build the decision tree. It can be used for bagging without the need to create of copy of X. If None a mask will be created that includes all samples.
X_argsorted : array-like, shape = [n_samples, n_features] or None
Each column of X_argsorted holds the row indices of X sorted according to the value of the corresponding feature in ascending order. I.e. X[X_argsorted[i, k], k] <= X[X_argsorted[j, k], k] for each j > i. If None, X_argsorted is computed internally. The argument is supported to enable multiple decision trees to share the data structure and to avoid re-computation in tree ensembles. For maximum efficiency use dtype np.int32.
sample_weight : array-like, shape = [n_samples] or 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. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.
check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
self : object
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
X : numpy array of shape [n_samples, n_features]
y : numpy array of shape [n_samples]
X_new : numpy array of shape [n_samples, n_features_new]
Get parameters for the estimator
deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
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.
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes, or the predict values.
- score(X, y)¶
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.
X : array-like, shape = [n_samples, n_features]
y : array-like, shape = [n_samples]
z : float
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :
- transform(X, threshold=None)¶
Reduce X to its most important features.
X : array or scipy sparse matrix of shape [n_samples, n_features]
The input samples.
threshold : string, float or None, optional (default=None)
The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if available, the object attribute threshold is used. Otherwise, “mean” is used by default.
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.