Release CRAN release 0.2.0 · ModelOriented/hstats

New major features

average_loss(): This new function calculates the average loss of a model for a given dataset, optionally grouped by a discrete vector. It supports the most important loss functions (squared error, Poisson deviance, Gamma deviance, Log loss, multivariate Log loss, absolute error, classification error), and allows for case weights. Custom losses can be passed as vector/matrix valued functions of signature f(obs, pred).
Note that such a custom function needs to return per-row losses, not their average.
perm_importance(): H-statistics are often calculated for important features only. To support this workflow, we have added permutation importance regarding the most important loss functions. Multivariate losses can be studied individually or collapsed over dimensions. The importance of feature groups can be studied as well. Note that the API of perm_importance() is different from the experimental pd_importance(), which is calculated from a "hstats" object.

hstats() now uses the default feature vector v = colnames(X), simplifying the API in most cases. The typical call is now hstats(object, X = Feature data).
h2_overall(), h2_pairwise(), h2_threeway(), pd_importance() by default do not plot results anymore. Set plot = TRUE to do so.

summary.hstats() now returns an object of class "summary_hstats" with its own print() method. Like this, one can use su <- summary() without printing to the console.
The output of summary.hstats() is printed slightly more compact.
plot.hstats() has recieved a rotate_x = FALSE argument for rotating x labels by 45 degrees.
plot.hstats() and summary.hstats() have received explicit arguments normalize, squared, sort, eps instead of passing them via ....
plot.hstats() now passes ... to geom_bar().
Slight speed-up of hstats() in the one-dimensional case.

Probabilistic {mlr3} classifiers did not work out-of-the box. This has been fixed.