sklearn.ensemble.partial_dependence.partial_dependence¶
- sklearn.ensemble.partial_dependence.partial_dependence(gbrt, target_variables, grid=None, X=None, percentiles=(0.05, 0.95), grid_resolution=100)[source]¶
Partial dependence of target_variables.
Partial dependence plots show the dependence between the joint values of the target_variables and the function represented by the gbrt.
Parameters: gbrt : BaseGradientBoosting
A fitted gradient boosting model.
target_variables : array-like, dtype=int
The target features for which the partial dependecy should be computed (size should be smaller than 3 for visual renderings).
grid : array-like, shape=(n_points, len(target_variables))
The grid of target_variables values for which the partial dependecy should be evaluated (either grid or X must be specified).
X : array-like, shape=(n_samples, n_features)
The data on which gbrt was trained. It is used to generate a grid for the target_variables. The grid comprises grid_resolution equally spaced points between the two percentiles.
percentiles : (low, high), default=(0.05, 0.95)
The lower and upper percentile used create the extreme values for the grid. Only if X is not None.
grid_resolution : int, default=100
The number of equally spaced points on the grid.
Returns: pdp : array, shape=(n_classes, n_points)
The partial dependence function evaluated on the grid. For regression and binary classification n_classes==1.
axes : seq of ndarray or None
The axes with which the grid has been created or None if the grid has been given.
Examples
>>> samples = [[0, 0, 2], [1, 0, 0]] >>> labels = [0, 1] >>> from sklearn.ensemble import GradientBoostingClassifier >>> gb = GradientBoostingClassifier(random_state=0).fit(samples, labels) >>> kwargs = dict(X=samples, percentiles=(0, 1), grid_resolution=2) >>> partial_dependence(gb, [0], **kwargs) (array([[-4.52..., 4.52...]]), [array([ 0., 1.])])