sklearn.ensemble.partial_dependence.plot_partial_dependence
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sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None, n_cols=3, grid_resolution=100, percentiles=(0.05, 0.95), n_jobs=None, verbose=0, ax=None, line_kw=None, contour_kw=None, **fig_kw)
[source]
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Partial dependence plots for features
.
The len(features)
plots are arranged in a grid with n_cols
columns. Two-way partial dependence plots are plotted as contour plots.
Read more in the User Guide.
Parameters: |
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gbrt : BaseGradientBoosting -
A fitted gradient boosting model. -
X : array-like, shape=(n_samples, n_features) -
The data on which gbrt was trained. -
features : seq of ints, strings, or tuples of ints or strings -
If seq[i] is an int or a tuple with one int value, a one-way PDP is created; if seq[i] is a tuple of two ints, a two-way PDP is created. If feature_names is specified and seq[i] is an int, seq[i] must be < len(feature_names). If seq[i] is a string, feature_names must be specified, and seq[i] must be in feature_names. -
feature_names : seq of str -
Name of each feature; feature_names[i] holds the name of the feature with index i. -
label : object -
The class label for which the PDPs should be computed. Only if gbrt is a multi-class model. Must be in gbrt.classes_ . -
n_cols : int -
The number of columns in the grid plot (default: 3). -
grid_resolution : int, default=100 -
The number of equally spaced points on the axes. -
percentiles : (low, high), default=(0.05, 0.95) -
The lower and upper percentile used to create the extreme values for the PDP axes. -
n_jobs : int or None, optional (default=None) -
None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. -
verbose : int -
Verbose output during PD computations. Defaults to 0. -
ax : Matplotlib axis object, default None -
An axis object onto which the plots will be drawn. -
line_kw : dict -
Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots. -
contour_kw : dict -
Dict with keywords passed to the matplotlib.pyplot.plot call. For two-way partial dependence plots. -
**fig_kw : dict -
Dict with keywords passed to the figure() call. Note that all keywords not recognized above will be automatically included here. |
Returns: |
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fig : figure -
The Matplotlib Figure object. -
axs : seq of Axis objects -
A seq of Axis objects, one for each subplot. |
Examples
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> fig, axs = plot_partial_dependence(clf, X, [0, (0, 1)])
...
Examples using sklearn.ensemble.partial_dependence.plot_partial_dependence