Predictive (Classification and Regression) Models Homologator
categorical.predictive.power
confusion.matrix
contr.dummy
contr.metric
contr.ordinal
create.model
create.prediction
dummy.data.frame
ROC.plot
scaler
select_on_class
train.adabag
train.bayes
train.gbm
train.glm
train.glmnet
train.knn
train.svm
predict.neuralnet.prmdt
predict.nnet.prmdt
predict.qda.prmdt
ROC.area
predict.glm.prmdt
predict.glmnet.prmdt
predict.knn.prmdt
predict.lda.prmdt
general.indexes
get_test_less_predict
get.default.parameters
gg_color
importance.plot
max_col
numeric_to_predict
numerical.predictive.power
original_model
Plotting prmdt models
predict.adabag.prmdt
predict.bayes.prmdt
predict.gbm.prmdt
predict.randomForest.prmdt
predict.rpart.prmdt
predict.svm.prmdt
predict.xgb.Booster
prediction.variable.balance
Printing prmdt index object
Printing prmdt prediction object
Printing prmdt models
train.lda
train.neuralnet
train.nnet
train.qda
train.randomForest
train.rpart
train.xgboost
Predictive (Classification and Regression) Models Homologator
type_correction
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) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>, ADA Boosting Esteban Alfaro, Matias Gamez, Noelia GarcĂa (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>, Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>, Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.