CRAN release 0.7.0

This release is intended to be the last before stable version 1.0.0.

Major change

Passing a background dataset bg_X is now optional.

If the explanation data X is sufficiently large (>= 50 rows), bg_X is derived as a random sample of bg_n = 200 rows from X. If X has less than bg_n rows, then simply
bg_X = X. If X has too few rows (< 50), you will have to pass an explicit bg_X.

Minor changes

• ranger() survival models now also work out-of-the-box without passing a tailored prediction function. Use the new argument survival = "chf" in kernelshap() and permshap() to distinguish cumulative hazards (default) and survival probabilities per time point.
• The resulting object of kernelshap() and permshap() now contain bg_X and bg_w used to calculate the SHAP values.

CRAN release 0.6.0

This release is intended to be the last before stable version 1.0.0.

Major changes

• Factor-valued predictions are not supported anymore.

Maintenance

• Fix CRAN note about unavailable link to gam::gam().
• Added dependency to {MASS} for calculating Moore-Penrose generalized matrix inverse.

CRAN release 0.5.0

New features

New additive explainer additive_shap() that works for models fitted via

• lm(),
• glm(),
• mgcv::gam(),
• mgcv::bam(),
• gam::gam(),
• survival::coxph(),
• survival::survreg().

The explainer uses predict(..., type = "terms"), a beautiful trick
used in fastshap::explain.lm(). The result will be identical to those returned by kernelshap() and permshap() but exponentially faster. Thanks David Watson for the great idea discussed in #130.

User visible changes

• permshap() now returns an object of class "kernelshap" to reduce the number of redundant methods.
• To distinguish which algorithm has generated the "kernelshap" object, the outputs of kernelshap(), permshap() (and additive_shap()) got an element "algorithm".
• is.permshap() has been removed.

CRAN release 0.4.1

Performance improvements

• Significant speed-up for pure data.frames, i.e., no data.tables or tibbles.
• Some small performance improvements, e.g., for factor predictions and univariate predictions.
• Slight speed-up of permshap() by caching calculations for the two special permutations of all 0 and all 1. Consequently, the m_exact component in the output is reduced by 2.

Documentation

• Rewrote many examples in the README.
• Added reference to Erik Strumbelj and Ivan Kononeko (2014).

CRAN release 0.4.0

Major changes

• Added permshap() to calculate exact permutation SHAP values. The function currently works for up to 14 features.
• Factor-valued predictions are now supported. Each level is represented by its dummy variable.

Other changes

• Slight speed-up.
• Integer valued case weights are now turned into doubles to avoid integer overflow.

CRAN release 0.3.8

API improvements

• Multi-output case: column names of predictions are now used as list names of the resulting S and SE lists.

Bug fixes

• {mlr3} probabilistic classification would not work out-of-the-box. This has been fixed (with corresponding example in the README) in #100
• The progress bar was initialized at 1 instead of 0. This is fixed.

Maintenance

• Added explanation of sampling Kernel SHAP to help file.
• In internal calculations, use explicit feature_names as dimnames (#96).

CRAN release 0.3.7

Maintenance

• Fixed problem in Latex math for MacOS.

CRAN release 0.3.6

Maintenance

• Improved help files and README

CRAN release 0.3.5

kernelshap 0.3.5

Maintenance

• New contributor: Przemyslaw Biecek - welcome on board!
• My new cozy home: https://github.com/ModelOriented/kernelshap
• Webpage created with "pkgdown"
• Introduced Github workflows
• More unit tests

Small visible changes

• Removed the ks_extract() function. It was designed to extract objects like the matrix S of SHAP values from the resulting "kernelshap" object x. We feel that the standard extraction options (x$S, x[["S"]], or getElement(x, "S")) are sufficient.
• Adding $(n \times K)$ matrix of predictions to the output, where $n$ is the number of rows in the explainer data X, and $K$ is the dimension of a single prediction (usually 1).
• Setting verbose = FALSE now does not suppress the warning on too large background data anymore. Use suppressWarnings() instead.

CRAN release 0.3.4

What's Changed

Documentation update

• New logo
• Better package description
• Better README