# BT Question Set P1-T2-20-20-2: m-fold cross-validation

### m-fold cross-validation

Our question P1-T2-20-2 (located at https://www.bionicturtle.com/forum/threads/p1-t2-20-20-regression-diagnostics-outliers-cooks-distance-m-fold-cross-validation-and-residual-diagnostics.23497/) mimics GARP’s approach in their Chapter 9; in particular their solution 9.14. However, it makes the mistake of using cross-validation to select the regression coefficients. The problem with this approach is simple: the entire dataset is not used to fit the model.

In the stackexhange Bogdanovist explains that “the purpose of cross-validation is not to come up with our final model. We don’t use these 5 instances of our trained model to do any real prediction. For that we want to use all the data we have to come up with the best model possible. The purpose of cross-validation is model checking, not model building.”

In this way training different linear regression coefficients is NOT training multiple models. In the example below, there are not three models, there is one model. The one model is the dead-simple univariate regression: lm(Y ~ X, data). The purpose of cross-validation is to estimate the error, it is NOT to calibrate the coefficients by using two-thirds (i.e., 6 of 9) of the dataset.

``````library(tidyverse)

obs <- tibble(
X = c(1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00),
Y = c(2.20, 3.50, 5.20, 2.80, 6.90, 8.80, 5.70, 11.30, 8.60)
)

testrows_M1 <- 1:6; testrows_M2 <- 4:9; testrows_M3 <- c(1:3, 7:9)
train_M1 <- obs[testrows_M1, ]; test_M1 <- obs[-testrows_M1, ]
train_M2 <- obs[testrows_M2, ]; test_M2 <- obs[-testrows_M2, ]
train_M3 <- obs[testrows_M3, ]; test_M3 <- obs[-testrows_M3, ]

model_M1 <- lm(Y ~ X, train_M1)
model_M2 <- lm(Y ~ X, train_M2)
model_M3 <- lm(Y ~ X, train_M3)

predict_M1 <- predict(model_M1, test_M1, type = "response")
predict_M2 <- predict(model_M2, test_M2, type = "response")
predict_M3 <- predict(model_M3, test_M3, type = "response")

error_M1 <- test_M1\$Y - predict_M1
error_M2 <- test_M2\$Y - predict_M2
error_M3 <- test_M3\$Y - predict_M3

CV_RSS_M1 <- sum(error_M1^2) # GARP's selection metric

df <- nrow(obs) - 2 # df = n - k, where k is no. of coefficients
# sample too small, not using

RMSE_M1 <- sqrt(mean(error_M1^2))
RMSE_M2 <- sqrt(mean(error_M2^2))
RMSE_M3 <- sqrt(mean(error_M3^2))

RMSE_M1``````
``##  2.545756``
``RMSE_M2``
``##  1.350539``
``RMSE_M3``
``##  1.82291``
``````library(caret)

model_cv <- train(
Y ~ X,
obs,
method = "lm",
trControl = trainControl(
method = "cv",
number = 3,
verboseIter = TRUE
)
)``````
``````## + Fold1: intercept=TRUE
## - Fold1: intercept=TRUE
## + Fold2: intercept=TRUE
## - Fold2: intercept=TRUE
## + Fold3: intercept=TRUE
## - Fold3: intercept=TRUE
## Aggregating results
## Fitting final model on full training set``````
``````# Notice how caret builds the final regression model on the full dataset.
# CV is to estimate the out-of-sample error; the folded models are "disposed"
# We are not REALLY comparing three different models; there is only one model here
# CV is to check the model not build the model
# And intuitively: we want to use all the data to fit the model.

model_cv\$finalModel``````
``````##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Coefficients:
## (Intercept)            X
##      1.4444       0.9333``````
``````model_full <- lm(Y~ X, obs)
model_full``````
``````##
## Call:
## lm(formula = Y ~ X, data = obs)
##
## Coefficients:
## (Intercept)            X
##      1.4444       0.9333`````` ##### David Harper
###### Founder & CEO of Bionic Turtle

I teach financial risk and enjoy learning data science