Regression.Batch.Fit: Batch Training, Prediction and Diagnostics of Regression Base Learners

Description

Batch Training, Prediction and Diagnostics of Regression Base Learners.

Usage

Regression.Batch.Fit(config.list, formula, data, ncores = 1
  , filemethod = FALSE, print.level = 1)
# S3 method for Regression.Batch.FitObj
predict(object, ..., ncores=1)
# S3 method for Regression.Batch.FitObj
plot(x, errfun=rmse.error, ...)

Value

Function Regression.Batch.Fit returns an object of class Regression.Batch.FitObj. Function predict.Regression.Batch.FitObj returns a matrix of predictions, each column corresponding to one base learner in the trained batch. Function plot.Regression.Batch.FitObj creates a plot of base learner errors over the training set, grouped by type of base learner (all configurations within a given base learner using the same symbol).

Arguments

config.list: List of configuration objects for batch of base learners to be trained.
formula: Formula objects expressing response and covariates.
data: Data frame containing response and covariates.
ncores: Number of cores to use during parallel training.
filemethod: Boolean indicator of whether to save estimation objects to disk or not.
print.level: Determining level of command-line output verbosity during training.
object: Object of class Regression.Batch.FitObj to make predictions for.
...: Arguments to be passed from/to other functions.
x: Object of class Regression.Batch.FitObj to plot.
errfun: Error function to use for calculating errors plotted.

Author

Alireza S. Mahani, Mansour T.A. Sharabiani

Examples

Run this code

data(servo)
myformula <- class~motor+screw+pgain+vgain
myconfigs <- make.configs("knn")
perc.train <- 0.7
index.train <- sample(1:nrow(servo), size = round(perc.train*nrow(servo)))
data.train <- servo[index.train,]
data.predict <- servo[-index.train,]
ret <- Regression.Batch.Fit(myconfigs, myformula, data.train, ncores=2)
newpred <- predict(ret, data.predict)