The bootstrap method can estimate the error rate in linear discriminant analysis. This method involves resampling data from the original dataset with replacement and then using the resampled data to calculate the error rate. The bootstrap method is an effective way of estimating the error rate as it considers the data's variability and provides a more accurate estimate than other methods. Additionally, it can estimate the error rate even when limited data is available.

Mayur Wanjari ,Thanks for that clarification

Can you explain how to do this using R language

Using R code, the bootstrap method can estimate the error rate in linear discriminant analysis. First, the data must be split into a training set and a test set and then normalized. The lda() function can then be used to run the calculations twice, with CV=TRUE for the first run to get predictions of class membership derived from leave-one-out cross-validation. The second run should use CV=FALSE to get predictions of class membership based on the entire training set. The true error rate estimator BT2 of the restricted linear or quadratic discriminant analysis can be calculated using the dawai package in R. Finally, resampling methods such as bootstrapping can be used to estimate the test error rate.

Any R code implementation...

Hi, dear Victorseelan Prof In Statistics

Check on "boot" package

Any model r program..like

Link...plz

Victorseelan Prof In Statistics

You can see below the following Examples :

# Usual bootstrap of the ratio of means using the city data

ratio