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.])])

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.partial_dependence.partial_dependence.html

doc_scikit_learn

2017-01-15 04:26:03

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