`NEWS.md`

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).

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
# Load the sample data
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.

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).

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).

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"`

.

`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()`

.

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.

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).New vignette on using

`vip`

with unsupported models (using the Keras API to TensorFlow as an example).Added basic sparklyr support.

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).