# vip 0.1.3.9000 Unreleased

## Enhancements

• Added support for SHAP-based feature importance which makes use of the recent fastshap package on CRAN. To use, simply call vi_shap() or vi() and specify method = "shap" (#87).

• Added support for "model_fit" objects from the parsnip package.

• Added support for "mvr" objects from the pls package (currently just calls caret::varImp()) (#35).

• The "lm" method for vi_model() gained a new type argument that allows users to use either (1) the raw coefficients if the features were properly standardized (type = "raw"), or (2) the absolute value of the corresponding t- or z-statistic (type = "stat", the default) (#77).

## User-visible changes

• The metric and pred_wrapper arguments to vi_permute() are no longer optional.

• The vip() function gained a new argument, geom, for specifying which type of plot to construct. Current options are geom = "col" (the default), geom = "point", geom = "boxplot", or geom = "violin" (the latter two only work for permutation-based importance with nsim > 1) (#79). Consequently, the bar argument has been removed.

• The vip() function gained two new arguments for specifying aesthetics: mapping and aesthetics (for fixed aesthetics like color = "red"). Consequently, the arguments color, fill, etc. have been removed (#80).

An example illustrating the above two changes is given below:

# Load required packages
library(ggplot2)  # for aes_string() function

data(mtcars)

# Fit a linear regression model
model <- lm(mpg ~ ., data = mtcars)

# Construct variable importance plots
p1 <- vip(model)
p2 <- vip(model, mapping = aes_string(color = "Sign"))
p3 <- vip(model, type = "dotplot")
p4 <- vip(model, type = "dotplot", mapping = aes_string(color = "Variable"),
aesthetics = list(size = 3))
grid.arrange(p1, p2, p3, p4, nrow = 2)
• The vip() function gained a new argument, include_type, which defaults to FALSE. If TRUE, the type of variable importance that was computed is included in the appropriate axis label. Set include_type = TRUE to revert to the old behavior.

## Miscellaneous

• Switched to the tinytest framework (#82).

• Minor documentation improvements.

## Bug fixes

• The internal (i.e., not exported) get_feature_names() function does a better job with "nnet" objects containing factors. It also does a better job at extracting feature names from model objects containing a "formula" component.

• vi_model() now works correctly for "glm" objects with non-Gaussian families (e.g., logistic regression) (#74).

• Added appropriate sparklyr version dependency (#59).

# vip 0.1.3 2019-07-03

## New functions

• Removed warnings from experimental functions.

• vi_permute() gained a type argument (i.e., type = "difference" or type = "ratio"); this argument can be passed via vi() or vip() as well.

• add_sparklines() creates an HTML widget to display variable importance scores with a sparkline representation of each features effect (i.e., its partial dependence function) (#64).

• Added support for the Olden and Garson algorithms with neural networks fit using the neuralnet, nnet, and RSNNS packages (#28).

• Added support for GLMNET models fit using the glmnet package (with and without cross-validation).

## Breaking changes

• The pred_fun argument in vi_permute() has been changed to pred_wrapper.

• The FUN argument to vi(), vi_pdp(), and vi_ice() has been changed to var_fun.

• Only the predicted class probabilities for the reference class are required (as a numeric vector) for binary classification when metric = "auc" or metric = "logloss".

## Minor changes

• vi_permute() gained a new logical keep argument. If TRUE (the default), the raw permutation scores from all nsim repetitions (provided nsim > 1) will be stored in an attribute called "raw_scores".

• vip() gained new logical arguments all_permutations and jitter which help to visualize the raw permutation scores for all nsim repetitions (provided nsim > 1).

• You can now pass a type argument to vi_permute() specifying how to compare the baseline and permuted performance metrics. Current choices are "difference" (the default) and "ratio".

• Improved documentation (especially for vi_permute() and vi_model()).

• Results from vi_model(), vi_pdp(), vi_ice(), and vi_permute() now have class "vi", making them easier to plot with vip().

# vip 0.1.2 2018-09-30

• Added nsim argument to vi_permute() for reducing the sampling variability induced by permuting each predictor (#36).

• Added sample_size and sample_frac arguments to vi_permute() for reducing the size of the training sample for every Monte Carlo repetition (#41).

• Greatly improved the documentation for vi_model() and the various objects it supports.

• New argument rank, which defaults to FALSE, available in vi() (#55).

• Added support for Spark (G)LMs.

• vi() is now a generic which makes adding new methods easier (e.g., to support DataRobot models).

• Bug fixes.

# vip 0.1.1 2018-09-27

• Fixed bug in get_feature_names.ranger() s.t. it never returns NULL; it either returns the feature names or throws an error if they cannot be recovered from the model object (#43).

• Added pkgdown site: https://github.com/koalaverse/vip.

• Changed truncate_feature_names argument of vi() to abbreviate_feature_names which abbreviates all feature names, rather than just truncating them.

• New generic vi_permute() for constructing permutation-based variable importance scores (#19).

• Fixed bug and unnecessary error check in vint() (#38).

• New vignette on using vip with unsupported models (using the Keras API to TensorFlow as an example).

# vip 0.1.0 2018-06-15

• Added support for XGBoost models (i.e., objects of class "xgb.booster").

• Added support for ranger models (i.e., objects of class "ranger").

• Added support for random forest models from the party package (i.e., objects of class "RandomForest").

• vip() gained a new argument, num_features, for specifying how many variable importance scores to plot. The default is set to 10.

• . was changed to _ in all argument names.

• vi() gained three new arguments: truncate_feature_names (for truncating feature names in the returned tibble), sort (a logical argument specifying whether or not the resulting variable importance scores should be sorted), and decreasing (a logical argument specifying whether or not the variable importance scores should be sorted in decreasing order).

• vi_model.lm(), and hence vi(), contains an additional column called Sign that contains the sign of the original coefficients (#27).

• vi() gained a new argument, scale, for scaling the variable importance scores so that the largest is 100. Default is FALSE (#24).

• vip() gained two new arguments, size and shape, for controlling the size and shape of the points whenever bar = FALSE (#9).

• Added support for "H2OBinomialModel", "H2OMultinomialModel", and, "H2ORegressionModel" objects (#8).

# vip 0.0.1 Unreleased

• Initial release.