PDP

class hidimstat.visualization.PDP(estimator, feature_names=None)

Bases: object

Partial Dependence Plot (PDP) visualization. This class is based on sklearn.inspection.partial_dependence to compute the partial dependence values and provides methods to plot 1D and 2D PDPs. For each realization of a feature or pair of features \(x_S\), the partial dependence \(f_S(x_S)\) is defined as \(f_S(x_S) = \mathbb{E}_{X_{-S}}[ f(x_S, X_{-S})]\), where \(X_{-S}\) denotes all features except those in \(S\).

Parameters:

estimatorobject

A fitted scikit-learn estimator implementing predict or predict_proba.

feature_nameslist of str, optional

Names of the features. If None, X0, X1, … will be used.

__init__(estimator, feature_names=None)

plot(X, features, cmap='viridis', **kwargs)

Plot the Partial Dependence Plot for the specified feature (1D) or pair of features (2D). The marginal distribution of the feature(s) is also displayed.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input data used to compute the partial dependence.

featuresint or list of int

The feature index (for 1D PDP) or list of two feature indices (for 2D PDP).

cmapstr, optional

The colormap to use for the plot (only for 2D PDP). Default is “viridis”.

**kwargsadditional keyword arguments

Additional keyword arguments passed to: - sns.lineplot for 1D PDP - ax.contour for 2D PDP

Examples using hidimstat.visualization.PDP

Visualization with Partial Dependency Plots

Visualization with Partial Dependency Plots