sklearn.tree.ExtraTreeRegressor¶
- class sklearn.tree.ExtraTreeRegressor(criterion='mse', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', random_state=None, min_density=None, compute_importances=None, max_leaf_nodes=None)¶
An extremely randomized tree regressor.
Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.
Warning: Extra-trees should only be used within ensemble methods.
See also
ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor
References
[R192] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006. Methods
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 this estimator. predict(X) Predict class or regression value for X. score(X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of this estimator. transform(X[, threshold]) Reduce X to its most important features. - __init__(criterion='mse', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', random_state=None, min_density=None, compute_importances=None, max_leaf_nodes=None)¶
- 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.
Returns: feature_importances_ : array, shape = [n_features]
- 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).
Parameters: X : array-like, shape = [n_samples, n_features]
The training input samples. Use dtype=np.float32 for maximum efficiency.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels in classification, real numbers in regression). In the regression case, use dtype=np.float64 and order='C' for maximum efficiency.
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.
Returns: self : object
Returns self.
- 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.
Parameters: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- predict(X)¶
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 of shape = [n_samples, n_features]
The input samples.
Returns: y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes, or the predict values.
- score(X, y, sample_weight=None)¶
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.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,)
True values for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.
- set_params(**params)¶
Set the parameters of this 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.
Parameters: 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.
Returns: X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.