8.6.2. sklearn.ensemble.RandomForestRegressor¶
- class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=1, min_samples_leaf=1, min_density=0.1, max_features='auto', bootstrap=True, compute_importances=False, oob_score=False, n_jobs=1, random_state=None, verbose=0)¶
A random forest regressor.
A random forest is a meta estimator that fits a number of classifical decision trees on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.
Parameters : n_estimators : integer, optional (default=10)
The number of trees in the forest.
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. Note: this parameter is tree-specific.
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. Note: this parameter is tree-specific.
min_samples_split : integer, optional (default=1)
The minimum number of samples required to split an internal node. Note: this parameter is tree-specific.
min_samples_leaf : integer, optional (default=1)
The minimum number of samples in newly created leaves. A split is discarded if after the split, one of the leaves would contain less then min_samples_leaf samples. Note: this parameter is tree-specific.
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). Note: this parameter is tree-specific.
max_features : int, string or None, optional (default=”auto”)
- 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.
Note: this parameter is tree-specific.
bootstrap : boolean, optional (default=True)
Whether bootstrap samples are used when building trees.
compute_importances : boolean, optional (default=True)
Whether feature importances are computed and stored into the feature_importances_ attribute when calling fit.
oob_score : bool
whether to use out-of-bag samples to estimate the generalization error.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores.
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.
verbose : int, optional (default=0)
Controlls the verbosity of the tree building process.
See also
DecisionTreeRegressor, ExtraTreesRegressor
References
[R65] - Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
Attributes
feature_importances_ array of shape = [n_features] The feature mportances (the higher, the more important the feature). oob_score_ float Score of the training dataset obtained using an out-of-bag estimate. oob_prediction_ array, shape = [n_samples] Prediction computed with out-of-bag estimate on the training set. Methods
fit(X, y) Build a forest of trees 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 regression target 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__(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=1, min_samples_leaf=1, min_density=0.1, max_features='auto', bootstrap=True, compute_importances=False, oob_score=False, n_jobs=1, random_state=None, verbose=0)¶
- fit(X, y)¶
Build a forest of trees from the training set (X, y).
Parameters : X : array-like of shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values (integers that correspond to classes in classification, real numbers in regression).
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.
Notes
This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.
- get_params(deep=True)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- predict(X)¶
Predict regression target for X.
The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.
Parameters : X : array-like of shape = [n_samples, n_features]
The input samples.
Returns : y: array of shape = [n_samples] :
The predicted 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 - 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]
Training set.
y : array-like, shape = [n_samples]
Returns : z : float
- set_params(**params)¶
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.
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.