HSALAS
4/19/2018 - 2:59 PM

Partial Dependence Plots

The partial dependence plot is calculated only after the model has been fit. The model is fit on real data.

If you are familiar with linear or logistic regression models, partial dependence plots can be interepreted similarly to the coefficients in those models. But partial dependence plots can capture more complex patterns from your data, and they can be used with any model. If you aren't familiar with linear or logistic regressions, don't get caught up on that comparison.

snippet.py
content_copyfile_download

# get_some_data is defined in hidden cell above.
X, y = get_some_data()
# scikit-learn originally implemented partial dependence plots only for Gradient Boosting models
# this was due to an implementation detail, and a future release will support all model types.
my_model = GradientBoostingRegressor()
# fit the model as usual
my_model.fit(X, y)
# Here we make the plot
my_plots = plot_partial_dependence(my_model,       
                                   features=[0, 2], # column numbers of plots we want to show
                                   X=X,            # raw predictors data.
                                   feature_names=['Distance', 'Landsize', 'BuildingArea'], # labels on graphs
                                   grid_resolution=10) # number of values to plot on x axis