R code: Back Transform from Caret’s preProcess()

This post gives a small R code for the back transformation of the caret’s preProcess() function, which is not implemented in caret R package yet. This is useful , for example, when we forecast stock prices using deep learning techniques such as the LSTM which requires normalized input data but we want to back transform it to the original scale.

Reverse Transform from Caret’s preProcess()

Caret R package provides a very convenient function, preProcess(), which transform a given data to a normalized or standardized one. However, it does not provide the back (or reverse) transformation function.


method = “center” or “scale” or c(“center”, “scale”)

x′ = (x−μx)/σx

method = “range”, rangeBounds = c(a, b)

These transformations are done by using preProcess() function in caret R package.

preProc <- preProcess(training, 
                      method = c("center", "scale"))
transformed <- predict(preProc, training)

Back Transformation

method = “center” or “scale” or c(“center”, “scale”)

method = “range”, rangeBounds = c(a, b)

These back transformations can accomplished by the following R code

# back transform using the object from the caret preProcess
back_preProc <- function(preProc, df_trans, digits = 10) {
    pp <- preProc
    nc <- ncol(df_trans); nr <- nrow(df_trans)
    av <- t(replicate(nr, pp$mean))
    st <- t(replicate(nr, pp$std))
    a  <- pp$rangeBounds
    x_max <- t(replicate(nr, pp$ranges[2,]))
    x_min <- t(replicate(nr, pp$ranges[1,]))
    if(sum(!is.na(match(c("center", "scale"), 
                        names(pp$method)))) == 2) {
        df <- df_trans*st + av
    } else if(sum(!is.na(match("center", 
                               names(pp$method)))) == 1) {
        df <- df_trans + av
    } else if(sum(!is.na(match("scale", 
                               names(pp$method)))) == 1) {
        df <- df_trans*st
    } else {
        df <- (df_trans-a[1])/(a[2]-a[1])*(x_max - x_min) + x_min
    return(round(df, digits))


An exercise is a range transformation between -1 and 1 with training and test sample data.

# Quantitative ALM, Financial Econometrics & Derivatives 
# ML/DL using R, Python, Tensorflow by Sang-Heon Lee 
# https://kiandlee.blogspot.com
# backtransform of caret::preProcess
graphics.off(); rm(list = ls())
# sample data
df <- data.frame(x = -10:10, y = -10:10*0.001)
# train/test splitting data
# In case of one-column dataframe, sub rows become a vector. 
# To avoid this and preserve a single-column data frame, 
# use drop=F option. 
df_train <- df[1:15,,drop=F]
df_test <- df[16:21,,drop=F]
# create transform funtion
preProc <- preProcess(df_train, method = "range", 
                      rangeBounds = c(-1, 1))
# transform
df_train_trans <- predict(preProc, df_train)
df_test_trans  <- predict(preProc, df_test)
# back transform of train data
df_train_back <- back_preProc(preProc, df_train_trans)
df_test_back  <- back_preProc(preProc, df_test_trans)
# print comparisons of returns
temp <- cbind(df_train, df_train_trans, df_train_back)
print("========= Train Data =========")
colnames(temp) <- c(
print("========= Test Data  =========")
temp <- cbind(df_test, df_test_trans, df_test_back)
colnames(temp) <- c(

Comparisons of the original, transformed, and back transformed data delivers the expected results.

The upper and lower bounds of the transfomed test data is not 1 and -1 since the raw data has a trend. To show a distinct result, I use a trending sample data.

For additional insight on this topic and to download the R scripts, visit https://kiandlee.blogspot.com/2022/10/r-code-back-transform-from-carets.html.

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