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(This article was first published on <strong><a href="https://mlr-org.github.io/mlr-210/"> mlr-org</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>mlr 2.10 is now on CRAN. Please update your package if you haven’t done so in a while.</p>
<p>Here is an overview of the changes:</p>
<p><span></span></p>
<h2>functions – general</h2>
<ul>
<li>fixed bug in resample when using predict = “train” (issue #1284)</li>
<li>update to irace 2.0 – there are algorithmic changes in irace that may affect<br>
performance</li>
<li>generateFilterValuesData: fixed a bug wrt feature ordering</li>
<li>imputeLearner: fixed a bug when data actually contained no NAs</li>
<li>print.Learner: if a learner hyperpar was set to value “NA” this was not<br>
displayed in printer</li>
<li>makeLearner, setHyperPars: if you mistype a learner or hyperpar name, mlr<br>
uses fuzzy matching to suggest the 3 closest names in the message</li>
<li>tuneParams: tuning with irace is now also parallelized, i.e., different<br>
learner configs are evaluated in parallel.</li>
<li>benchmark: mini fix, arg ‘learners’ now also accepts class strings</li>
<li>object printers: some mlr printers show head previews of data.frames.<br>
these now also print info on the total nr of rows and cols and are less confusing</li>
<li>aggregations: have better properties now, they know whether they require training or<br>
test set evals</li>
<li>the filter methods have better R docs</li>
<li>filter randomForestSRC.var.select: new arg “method”</li>
<li>filter mrmr: fixed some smaller bugs and updated properties</li>
<li>generateLearningCurveData: also accepts single learner, does not require a list</li>
<li>plotThreshVsPerf: added “measures” arg</li>
<li>plotPartialDependence: can create tile plots with joint partial dependence<br>
on two features for multiclass classification by facetting across the classes</li>
<li>generatePartialDependenceData and generateFunctionalANOVAData: expanded<br>
“fun” argument to allow for calculation of weights</li>
<li>new “?mlrFamilies” manual page which lists all families and the functions<br>
belonging to it</li>
<li>we are converging on data.table as a standard internally, this should not<br>
change any API behavior on the outside, though</li>
<li>generateHyperParsEffectData and plotHyperParsEffect now support more than 2<br>
hyperparameters</li>
<li>linear.correlation, rank.correlation, anova.test: use Rfast instead of<br>
FSelector/custom implementation now, performance should be much better</li>
<li>use of our own colAUC function instead of the ROCR package for AUC calculation<br>
to improve performance</li>
<li>we output resample performance messages for every iteration now</li>
<li>performance improvements for the auc measure</li>
<li>createDummyFeatures supports vectors now</li>
<li>removed the pretty.names argument from plotHyperParsEffect – labels can be set<br>
though normal ggplot2 functions on the returned object</li>
<li>Fixed a bad bug in resample, the slot “runtime” or a ResampleResult,<br>
when the runtime was measured not in seconds but e.g. mins. R measures then potentially in mins,<br>
but mlr claimed it would be seconds.</li>
<li>New “dummy” learners (that disregard features completely) can be fitted now for baseline comparisons,<br>
see “featureless” learners below.</li>
</ul>
<h2>functions – new</h2>
<ul>
<li>filter: randomForest.importance</li>
<li>generateFeatureImportanceData: permutation-based feature importance and local<br>
importance</li>
<li>getFeatureImportanceLearner: new Learner API function</li>
<li>getFeatureImportance: top level function to extract feature importance<br>
information</li>
<li>calculateROCMeasures</li>
<li>calculateConfusionMatrix: new confusion-matrix like function that calculates<br>
and tables many receiver operator measures</li>
<li>makeLearners: create multiple learners at once</li>
<li>getLearnerId, getLearnerType, getLearnerPredictType, getLearnerPackages</li>
<li>getLearnerParamSet, getLearnerParVals</li>
<li>getRRPredictionList</li>
<li>addRRMeasure</li>
<li>plotResiduals</li>
<li>getLearnerShortName</li>
<li>mergeBenchmarkResults</li>
</ul>
<h2>functions – renamed</h2>
<ul>
<li>Renamed rf.importance filter (now deprecated) to randomForestSRC.var.rfsrc</li>
<li>Renamed rf.min.depth filter (now deprecated) to randomForestSRC.var.select</li>
<li>Renamed getConfMatrix (now deprecated) to calculateConfusionMatrix</li>
<li>Renamed setId (now deprecated) to setLearnerId</li>
</ul>
<h2>functions – removed</h2>
<ul>
<li>mergeBenchmarkResultLearner, mergeBenchmarkResultTask</li>
</ul>
<h2>learners – general</h2>
<ul>
<li>classif.ada: fixed some param problem with rpart.control params</li>
<li>classif.cforest, regr.cforest, surv.cforest:<br>
removed parameters “minprob”, “pvalue”, “randomsplits”<br>
as these are set internally and cannot be changed by the user</li>
<li>regr.GPfit: some more params for correlation kernel</li>
<li>classif.xgboost, regr.xgboost: can now properly handle NAs (property was missing and other problems), added “colsample_bylevel” parameter</li>
<li>adapted {classif,regr,surv}.ranger parameters for new ranger version</li>
</ul>
<h2>learners – new</h2>
<ul>
<li>multilabel.cforest</li>
<li>surv.gbm</li>
<li>regr.cvglmnet</li>
<li>{classif,regr,surv}.gamboost</li>
<li>classif.earth</li>
<li>{classif,regr}.evtree</li>
<li>{classif,regr}.evtree</li>
</ul>
<h2>learners – removed</h2>
<ul>
<li>classif.randomForestSRCSyn, regr.randomForestSRCSyn: due to continued stability issues</li>
</ul>
<h2>measures – new</h2>
<ul>
<li>ssr, qsr, lsr</li>
<li>rrse, rae, mape</li>
<li>kappa, wkappa</li>
<li>msle, rmsle</li>
</ul>
<p>
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