learners: List of supported learning algorithms.
Description
{
Boosting from ada package: ada}
- classif.boosting
{
Boosting from adabag package: boosting
Note that xval has been set to 0 by default for speed.}
- classif.blackboost
{
Gradient boosting with regression trees from mboost package: blackboost}
- classif.ctree
{
Conditional Inference Trees from party package: ctree}
- classif.fnn
{
Fast k-Nearest Neighbor from FNN package: knn}
- classif.gbm
{
Gradient boosting machine from gbm package: gbm}
- classif.geoDA
{
Geometric Predictive Discriminant Analysis from DiscriMiner package: geoDA}
- classif.glmboost
{
Boosting for GLMs from mboost package: glmboost
Note that family has been set to Binomial() by default.}
- classif.IBk
{
K-nearest neighbours from RWeka package: IBk}
- classif.J48
{
J48 Decision Trees from RWeka package: J48}
Note that NAs are directly passed to WEKA with na.action = na.pass.
- classif.JRip
{
Propositional Rule Learner from RWeka package: JRip}
Note that NAs are directly passed to WEKA with na.action = na.pass.
- classif.kknn
{
k-Nearest Neighbor from kknn package: kknn}
- classif.knn
{
k-Nearest Neighbor from class package: knn}
- classif.ksvm
{
Support Vector Machines from kernlab package: ksvm
Note that kernel parameters have to be passed directly and not by using the kpar list in ksvm.
Note that fit has been set to FALSE by default for speed.}
- classif.lda
{
Linear Discriminant Analysis from MASS package: lda}
- classif.LiblineaRBinary
{
Regularized Binary Linear Predictive Models Estimation from LiblineaR package: LiblineaR
Note that this model subsumes the types 1,2,3,5}
- classif.LiblineaRLogReg
{
Regularized Logistic Regression from LiblineaR package: LiblineaR
Note that this model subsumes type 0,6,7.}
- classif.LiblineaRMultiClass
{
Multi-class Support Vector Classification by Crammer and Singer from LiblineaR package: LiblineaR
Note that this model is type 4.}
- classif.linDA
{
Linear Discriminant Analysis from DiscriMiner package: linDA}
- classif.logreg
{
Logistic Regression from stats package: glm}
- classif.lssvm
{
Least Squares Support Vector Machine from kernlab package: lssvm
Note that fitted has been set to FALSE by default for speed.}
- classif.lvq1
{
Learning Vector Quantization from class package: lvq1}
- classif.mda
{
Mixture Discriminant Analysis from mda package: mda
Note that keep.fitted has been set to FALSE by default for speed.}
- classif.multinom
{
Multinomial Regression from nnet package: multinom}
- classif.naiveBayes
{
Naive Bayes from e1071 package: naiveBayes}
- classif.nnet
{
Neural Network from nnet package: nnet
Note that size has been set to 3 by default.}
- classif.OneR
{
1-R classifier from RWeka package: OneR
Note that NAs are directly passed to WEKA with na.action = na.passi}.
- classif.PART
{
PART decision lists from RWeka package: PART
Note that NAs are directly passed to WEKA with na.action = na.pass}.
- classif.plr
{
Logistic regression with a L2 penalty from stepPlr package: plr
Note that AIC and BIC penalty types can be selected via the new parameter cp.type}.
- classif.plsDA
{
Partial Least Squares (PLS) Discriminant Analysis from DiscriMiner package: plsDA}
- classif.qda
{
Quadratic Discriminant Analysis from MASS package: qda}
- classif.quaDA
{
Quadratic Discriminant Analysis from DiscriMiner package: quaDA}
- classif.randomForest
{
Random Forest from randomForest package: randomForest.
The argument fix.factors restores the factor levels seen in the training data before prediction to circumvent
randomForest's internal sanity checks. Default is FALSE.}
- classif.rda
{
Regularized Discriminant Analysis from klaR package: rda
Note that estimate.error has been set to FALSE by default for speed.}
- classif.rpart
{
Decision Tree from rpart package: rpart
Note that xval has been set to 0 by default for speed.}
- classif.svm
{
Support Vector Machines (libsvm) from e1071 package: svm}cr
- Gradient boosting with regression trees from mboost package:
blackboost - Regression Splines from crs package:
crs - Multivariate Adaptive Regression Splines from earth package:
earth - Fast k-Nearest Neighbor from FNN package:
knn - Gradient boosting machine from gbm package:
gbm
Note that distribution has been set to gaussian by default. - K-nearest neighbours from RWeka package:
IBk - K-Nearest-Neighbor regression from kknn package:
kknn - Kriging from DiceKriging package:
km - Support Vector Machines from kernlab package:
ksvm
Note that kernel parameters have to be passed directly and not by using the kpar list in ksvm.
Note that fit has been set to FALSE by default for speed. - Lasso regression from penalized package:
penalized - Simple linear regression from stats package:
lm - Multivariate Adaptive Regression Splines from mda package:
mars - Model-based recursive partitioning yielding a tree with fitted models associated
with each terminal node from party package:
mob - Neural Network from nnet package:
nnet
Note that size has been set to 3 by default. - Principal component regression from pls package:
pcr
Note that model has been set to FALSE by default for speed. - Random Forest from randomForest package:
randomForest.
The argument fix.factors restores the factor levels seen in the training data before prediction to circumvent
randomForest's internal sanity checks. Default is FALSE. - Ridge regression from penalized package:
penalized - Decision Tree from rpart package:
rpart
Note that xval has been set to 0 by default for speed. - Response surface regression from rsm package:
rsm
Note that you select the order of the regression by using modelfun = "FO" (first order), "TWI" (two-way interactions, this is with 1st oder terms!) and "SO" (full second order) - Relevance Vector Machine from package kernlab:
rvm
Note that kernel parameters have to be passed directly and not by using the kpar list in rvm.
Note that fit has been set to FALSE by default for speed. - Cox proportional hazards model with componentwise likelhood based boosting from CoxBoost package:
CoxBoost - Cox proportional hazard model from survival package:
coxph - GLM with regularization from glmnet package:
glmnet - Random Forests for Survival from randomForestSRC package:
randomForestSRC
item
- regr.crs
- regr.earth
- regr.fnn
- regr.gbm
- regr.IBk
- regr.kknn
- regr.km
- regr.ksvm
- regr.penalized.lasso
- regr.lm
- regr.mars
- regr.mob
- regr.nnet
- regr.pcr
- regr.randomForest
- regr.penalized.ridge
- regr.rpart
- regr.rsm
- regr.rvm
- surv.coxph
- surv.glmnet
- surv.randomForestSRC