Add new optional parameter probFunction to glex() which specifies the probability function for weighting/marginalization of the leaves (PR#17).
By default, glex() now uses the empirical marginal probabilities to perform the weighting. Previously, the weighting of the leaves was done based on a path-dependent method.
Add theme_glex() as a default theme to all plots.
This is almost identical to [ggplot2::theme_minimal()] aside from increased base font size and convenience flags to toggle vertical and horizontal grid lines.
Limit max_interaction in glex.xgb.Booster to max_depth parameter of xgboost model. If max_depth is not set during model fit, the default value of 6 is assumed. This prevents glex from returning spurious higher-order interactions containing values numerically close to 0.
Extend plot functions to multiclass classification. In most cases that means facetting by the target class.
Overhaul glex_explain to a waterfall plot showing the SHAP decomposition for given predictors.
autoplot.glex_vi gains a max_interaction argument in line with glex_explain, and now similarly aggregates terms that either fall below threshold or exceed max_interaction.
Add glex.print for a more compact output in case of large numbers of terms.
glex 0.3.0
Added plotting functions for main, 2- and 3-degree interaction terms