Nothing
nestcv.glmnet()
verbose
in nestcv.train()
, nestcv.glmnet()
and
outercv()
to show progress.multicore_fork
in nestcv.train()
and outercv()
to allow
choice of parallelisation between forked multicore processing using mclapply
or non-forked using parLapply
. This can help prevent errors with certain
multithreaded caret models e.g. model = "xgbTree"
.one_hot()
changed all_levels
argument default to FALSE
to be
compatible with regression models by default.lm_filter()
full results tablelm_filter()
where variables with zero variance were
incorrectly reporting very low p-values in linear models instead of returning
NA
. This is due to how rank deficient models are handled by
RcppEigen::fastLmPure
. Default method for fastLmPure
has been changed to 0
to allow detection of rank deficient models.weight()
caused by NA
. Allow weight()
to tolerate character
vectors.keep_factors
option has been added
to filters to control filtering of factors with 3 or more levels.one_hot()
for fast one-hot encoding of factors and character columns
by creating dummy variables.stat_filter()
which applies univariate filtering to dataframes with
mixed datatype (continuous & categorical combined).anova_filter()
from Rfast::ftests()
to
matrixTests::col_oneway_welch()
for much better accuracynestcv.train()
(Matt Siggins
suggestion)n_inner_folds
argument to nestcv.train()
to make it easier to set
the number of inner CV folds, and inner_folds
argument which enables setting
the inner CV fold indices directly (suggestion Aline Wildberger)plot_shap_beeswarm()
caused by change in fastshap 0.1.0 output
from tibble to matrixnestcv.train()
pass_outer_folds
to both nestcv.glmnet
and nestcv.train
:
this enables passing of passing of outer CV fold indices stored in outer_folds
to the final round of CV. Note this can only work if n_outer_folds
= number of
inner CV folds and balancing is not applied so that y
is a consistent length.nfolds
for final CV equals n_inner_folds
in nestcv.glmnet()
plot_var_stability()
to be more user friendlytop
argument to shap plotsfastshap
for calculating SHAP values.force_vars
argument to glmnet_filter()
ranger_filter()
nestcv.train()
from models such as gbm
. This fixes
multicore bug when using standard R gui on mac/linux.nestcv.glmnet()
model has 0 or 1 coefficients.nestedcv
models now return xsub
containing a subset of the predictor
matrix x
with filtered variables across outer folds and the final fitboxplot_model()
no longer needs the predictor matrix to be specified as it
is contained in xsub
in nestedcv
modelsboxplot_model()
now works for all nestedcv
model typesvar_stability()
to assess variance and stability of variable
importance across outer folds, and directionality for binary outcomeplot_var_stability()
to plot variable stability across outer
foldsfinalCV = NA
option which skips fitting the final model completely. This
gives a useful speed boost if performance metrics are all that is needed.model
argument in outercv
now prefers a character value instead of a
function for the model to be fittedoutercv
nestcv.train
which improves error detection
in caret. So nestcv.train
can be run in multicore mode straightaway.nestcv.glmnet
nestcv.glmnet
outer_train_predict
argument to enable saving of predictions on outer
training foldstrain_preds
to obtain outer training fold predictionstrain_summary
to show performance metrics on outer training
foldssmote()
SuperLearner
packagenestcv.train
and
nestcv.glmnet
nestcv.train
for caret models with tuning parameters which are
factorsnestcv.train
for caret models using regressionnestcv.train
and nestcv.glmnet
to tune final model
parameters using a final round of CV on the whole datasetnestcv.train
and outercv
randomsample()
to handle class imbalance using random over/undersamplingsmote()
for SMOTE algorithm for increasing minority class databoot_ttest()
nestcv.glmnet()
is mean of best lambdas on log scaleplot_varImp
for plotting variable importance for nestcv.glmnet
final
modelsnestcv.glmnet()
cva.glmnet()
plot.cva.glmnet
alphaSet
in plot.cva.glmnet
train
function of caret
filterFUN
is no longer done through ...
but with a list of arguments passed through a
new argument filter_options
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