CRAN release 0.3.0
This is intended to be the last version before 1.0.0.
Visible changes
- Grid of
ice()
andpartial_dep()
: So far, the default grid strategy "uniform" usedpretty()
to generate the evaluation points. To provide more predictable grid sizes, and to be more in line with other implementations of partial dependence and ICE, we now useseq()
to create the uniform grid. h2_pairwise()
andh2_threeway()
will now also include 0 values. Usezero = FALSE
to drop them, see below. The padding with 0 is done at no computational cost, and will affect only up topairwise_m
andthreeway_m
features.hstats()
: The default number of features considered for three-way interactions has been changed fromthreeway_m = pairwise_m
to the more cautiousthreeway_m = min(pairwise_m, 5L)
. Furthermore,threeway_m
is capped atpairwise_m
.- The
print()
method ofsummary.hstats()
is less verbose.
Improvements
h2_overall()
,h2_pairwise()
,h2_threeway()
,plot.hstats()
, andsummary.hstats()
have received an argumentzero = TRUE
. Set toFALSE
to drop statistics having value 0.perm_importance()
andaverage_loss()
will now recycle a univariate response when combined with multivariate predictions. This is useful, e.g., when the prediction function represents the predictions of multiple models that should be evaluated against a common response.
Bug fixes
- All progress bars were initialized 1 step too late.
perm_importance()
andaverage_loss()
would fail for "mlogloss" in case the responsey
was univariate and non-factor/non-character.
Other changes
- All available H-statistics are now calculated within
hstats()
and attached to the resulting object. Each statistic is stored as list with numerator and denominator matrices/vectors. The functionsh2()
,h2_overall()
,h2_pairwise()
, andh2_threeway()
,print.hstats()
,summary().hstats()
,plot.hstats()
will use these without having to recalculate the required numerators and denominators. The results, however, are unchanged.