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traineR (version 2.2.11)

Predictive (Classification and Regression) Models Homologator

Description

Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004) , Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) , ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) , Extreme Gradient Boosting Chen & Guestrin (2016) , Random Forest Breiman (2001) , Neural Networks Venables, W. N., & Ripley, B. D. (2002) , Support Vector Machines Bennett, K. P. & Campbell, C. (2000) , Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) , Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) .

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Version

Install

install.packages('traineR')

Monthly Downloads

459

Version

2.2.11

License

GPL (>= 2)

Maintainer

Oldemar Rodriguez

Last Published

January 27th, 2026

Functions in traineR (2.2.11)

prediction.variable.balance

prediction.variable.balance
get_test_less_predict

get_test_less_predict
print.prmdt

Printing prmdt models
print.prediction.prmdt

Printing prmdt prediction object
train.bayes

train.bayes
predict.neuralnet.prmdt

predict.neuralnet.prmdt
print.indexes.prmdt

Printing prmdt index object
train.adabag

train.adabag
train.glm

train.glm
train.gbm

train.gbm
predict.lda.prmdt

predict.lda.prmdt
train.nnet

train.nnet
train.qda

train.qda
predict.knn.prmdt

predict.knn.prmdt
traineR

Predictive (Classification and Regression) Models Homologator
type_correction

type_correction
predict.svm.prmdt

predict.svm.prmdt
predict.randomForest.prmdt

predict.randomForest.prmdt
original_model

original_model
plot.prmdt

Plotting prmdt models
predict.qda.prmdt

predict.qda.prmdt
predict.nnet.prmdt

predict.nnet.prmdt
predict.xgb.Booster.prmdt

predict.xgb.Booster
predict.gbm.prmdt

predict.gbm.prmdt
gg_color

gg_color
predict.glm.prmdt

predict.glm.prmdt
train.xgboost

train.xgboost
train.svm

train.svm
predict.rpart.prmdt

predict.rpart.prmdt
train.glmnet

train.glmnet
train.lda

train.lda
scaler

scaler
train.neuralnet

train.neuralnet
select_on_class

select_on_class
train.rpart

train.rpart
train.randomForest

train.randomForest
train.knn

train.knn
ROC.plot

ROC.plot
dummy.data.frame

dummy.data.frame
contr.dummy

contr.dummy
confusion.matrix

confusion.matrix
contr.ordinal

contr.ordinal
ROC.area

ROC.area
contr.metric

contr.metric
categorical.predictive.power

categorical.predictive.power
importance.plot

importance.plot
numerical.predictive.power

numerical.predictive.power
predict.glmnet.prmdt

predict.glmnet.prmdt
predict.adabag.prmdt

predict.adabag.prmdt
general.indexes

general.indexes
predict.bayes.prmdt

predict.bayes.prmdt
max_col

max_col
create.model

create.model
get.default.parameters

get.default.parameters
create.prediction

create.prediction
numeric_to_predict

numeric_to_predict