mlr: Machine Learning in R

Help pages for package ‘mlr’ version 0.3.180

A B C D G I K L M N P Q R S T W misc

-- A --

adaboost adaboost
adaboost-class adaboost
as.character,classif.task-method Conversion to string.
as.character,data.desc-method Conversion to string.
as.character,regr.task-method Conversion to string.
as.character,resample.result-method Conversion to string.
as.character,wrapped.learner-method Conversion to string.
as.character,wrapped.model-method Conversion to string.

-- B --

benchmark benchmark
benchmark,learn.task,resample.instance,resample.desc,list,list,logical-method benchmark
blackboost.regr blackboost.regr
blackboost.regr-class blackboost.regr
bs.desc bs.desc
bs.desc-class bs.desc
bs.instance bs.instance
bs.instance-class bs.instance

-- C --

classif.task classif.task
classif.task-class classif.task
classif.task.getter Getter for classif.task
combine.ranges combine.ranges
conf.matrix conf.matrix
cv.desc cv.desc
cv.desc-class cv.desc
cv.instance cv.instance
cv.instance-class cv.instance

-- D --

data.desc data.desc
data.desc-class data.desc

-- G --

gbm.regr gbm.regr
gbm.regr-class gbm.regr
getter,classif.task-method Getter for classif.task
getter,learn.task-method Getter for learn.task
getter,resample.desc-method Getter for resample.desc
getter,resample.instance-method Getter for resample.instance
getter,resample.result-method Getter for resample.result
getter,wrapped.model-method Getter for wrapped.model

-- I --

initialize,adaboost-method Adaboost Constructor
initialize,blackboost.regr-method Boosting Constructor
initialize,bs.desc-method Create description object for bootstrapping.
initialize,bs.instance-method bs.instance constructor
initialize,classif.task-method classif.task constructor
initialize,cv.desc-method Create description object for cross-validation.
initialize,cv.instance-method cv.instance constructor
initialize,data.desc-method data.desc constructor
initialize,gbm.regr-method GBM Constructor
initialize,kernlab.svm.classif-method SVM Constructor
initialize,kknn.classif-method kNN (classification) Constructor
initialize,kknn.regr-method kNN (regression) Constructor
initialize,lda-method LDA Constructor
initialize,learn.task-method learn.task constructor
initialize,loclda-method loclda Constructor
initialize,logreg-method Logistic Regression Constructor
initialize,mda-method MDA Constructor
initialize,naiveBayes-method Naive Bayes Constructor
initialize,nnet.multinom-method Multinomial Regression Constructor
initialize,penalized.lasso-method Lasso Regression Constructor
initialize,penalized.ridge-method Ridge Regression Constructor
initialize,qda-method QDA Constructor
initialize,randomForest.classif-method Random Forest Constructor
initialize,rda-method RDA Constructor
initialize,regr.task-method regr.task constructor
initialize,resample.instance-method This is mainly for internal use, you only need to use this, when you extend resample...
initialize,rpart.classif-method rpart Constructor
initialize,stats.lm-method LM Constructor
initialize,subsample.desc-method Create description object for subsampling.
initialize,subsample.instance-method subsample.instance constructor
initialize,wrapped.learner-method wrapped.learner constructor
initialize,wrapped.learner.classif-method wrapped.learner.classif constructor

-- K --

kernlab.svm.classif kernlab.svm.classif
kernlab.svm.classif-class kernlab.svm.classif
kknn.classif kknn.classif
kknn.classif-class kknn.classif
kknn.regr kknn.regr
kknn.regr-class kknn.regr

-- L --

lda lda
lda-class lda
learn.task learn.task
learn.task-class learn.task
learn.task.getter Getter for learn.task
learner.props Description object for the features of a learning algorithm.
learner.props-class Description object for the features of a learning algorithm.
loclda loclda
loclda-class loclda
logreg logreg
logreg-class logreg

-- M --

make.bs.desc make.bs.desc
make.bs.instance make.bs.instance
make.bs.instance,numeric,numeric-method make.bs.instance
make.classif.task make.classif.task
make.classif.task,character,formula,data.frame,numeric,character-method make.classif.task
make.cv.desc make.cv.desc
make.cv.instance make.cv.instance
make.cv.instance,numeric,numeric-method make.cv.instance
make.regr.task make.regr.task
make.regr.task,character,formula,data.frame,numeric-method make.regr.task
make.resample.instance Mainly for internal use.
make.resample.instance,resample.desc,numeric-method Mainly for internal use.
make.subsample.desc make.subsample.desc
make.subsample.instance make.subsample.instance
make.subsample.instance,numeric,numeric,numeric-method make.subsample.instance
mda mda
mda-class mda

-- N --

naiveBayes naiveBayes
naiveBayes-class naiveBayes
nnet.multinom nnet.multinom
nnet.multinom-class nnet.multinom

-- P --

penalized.lasso penalized.lasso
penalized.lasso-class penalized.lasso
penalized.ridge penalized.ridge
penalized.ridge-class penalized.ridge
performance performance
performance,ANY,ANY,numeric,list-method performance
predict predict
predict,classif.task-method predict
predict,learn.task-method predict
predict,regr.task-method predict

-- Q --

qda qda
qda-class qda

-- R --

randomForest.classif randomForest.classif
randomForest.classif-class randomForest.classif
rda rda
rda-class rda
regr.task classif.task
regr.task-class classif.task
resample.desc resample.desc
resample.desc-class resample.desc
resample.desk.getter Getter for resample.desc
resample.fit resample.fit
resample.fit,learn.task,resample.instance,list,character,logical,character-method resample.fit
resample.instance resample.instance
resample.instance-class resample.instance
resample.instance.getter Getter for resample.instance
resample.performance performance
resample.performance,learn.task,resample.instance,resample.result,list-method performance
resample.result resample.result
resample.result-class resample.result
resample.result.getter Getter for resample.result
rpart.classif rpart.classif
rpart.classif-class rpart.classif

-- S --

set.predict.par set.predict.par
set.predict.par,learn.task-method set.train.par
set.predict.par,wrapped.learner-method set.predict.par
set.predict.par-methods set.predict.par
set.train.par set.train.par
set.train.par set.train.par
set.train.par,learn.task-method set.train.par
set.train.par,wrapped.learner-method set.train.par
set.train.par-methods set.train.par
stats.lm stats.lm
stats.lm-class stats.lm
subsample.desc subsample.desc
subsample.desc-class subsample.desc
subsample.instance subsample.instance
subsample.instance-class subsample.instance

-- T --

train train
train-methods train
train.learner train.learner
train.learner,kernlab.svm.classif,formula,data.frame,numeric,list-method Overwritten, to allow direct passing of kernel hyperparameters.
train.learner,penalized.lasso,formula,data.frame,numeric,list-method Overwritten, to allow "lambda" instead of "lambda1" as parameter name.
train.learner,penalized.ridge,formula,data.frame,numeric,list-method Overwritten, to allow "lambda" instead of "lambda2" as parameter name.
train.learner,wrapped.learner,formula,data.frame,numeric,list-method train.learner
tune tune

-- W --

wrapped.learner wrapped.learner
wrapped.learner-class wrapped.learner
wrapped.learner.classif wrapped.learner.classif
wrapped.learner.classif-class wrapped.learner.classif
wrapped.learner.regr wrapped.learner.regr
wrapped.learner.regr-class wrapped.learner.regr
wrapped.model wrapped.model
wrapped.model-class wrapped.model
wrapped.model.getter Getter for wrapped.model

-- misc --

[,classif.task-method Getter for classif.task
[,learn.task-method Getter for learn.task
[,resample.desc-method Getter for resample.desc
[,resample.instance-method Getter for resample.instance
[,resample.result-method Getter for resample.result
[,wrapped.model-method Getter for wrapped.model