Compute variance-based variable importance using a simple feature importance ranking measure (FIRM) approach; for details, see Greenwell et al. (2018) and Scholbeck et al. (2019).
vi_firm(object, ...)# S3 method for default
vi_firm(object, feature_names, FUN = NULL, var_fun = NULL, ice = FALSE, ...)
A tidy data frame (i.e., a "tibble" object) with two columns,
Variable and Importance, containing the variable name and its
associated importance score, respectively.
A fitted model object (e.g., a "randomForest" object).
Additional optional arguments to be passed on to
partial.
Character string giving the names of the predictor variables (i.e., features) of interest.
Deprecated. Use var_fun instead.
List with two components, "cat" and "con",
containing the functions to use to quantify the variability of the feature
effects (e.g., partial dependence values) for categorical and continuous
features, respectively. If NULL, the standard deviation is used for
continuous features. For categorical features, the range statistic is used
(i.e., (max - min) / 4). Only applies when method = "firm".
Logical indicating whether or not to estimate feature effects
using individual conditional expectation (ICE) curves.
Only applies when method = "firm". Default is FALSE. Setting
ice = TRUE is preferred whenever strong interaction effects are
potentially present.
This approach to computing VI scores is based on quantifying the relative "flatness" of the effect of each feature. Feature effects can be assessed using partial dependence plots (PDPs) or individual conditional expectation (ICE) curves. These approaches are model-agnostic and can be applied to any supervised learning algorithm. By default, relative "flatness" is defined by computing the standard deviation of the y-axis values for each feature effect plot for numeric features; for categorical features, the default is to use range divided by 4. This can be changed via the `var_fun` argument. See Greenwell et al. (2018) for details and additional examples.
Greenwell, B. M., Boehmke, B. C., and McCarthy, A. J. A Simple and Effective Model-Based Variable Importance Measure. arXiv preprint arXiv:1805.04755 (2018).
Scholbeck, C. A. Scholbeck, and Molnar, C., and Heumann C., and Bischl, B., and Casalicchio, G. Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. arXiv preprint arXiv:1904.03959 (2019).