Hello. I need help creating linear regression models in R Studio. Here is what I am trying to do:

1. The pixel values in both the training set ranges in [0, 255], thus divide them by 255 to normalize them between [0, 1]. 2. Train Logistic Regression on the training set.

3. Train LDA and QDA on the training set

3. Train the Naive Bayes classifier by tuning the bandwidth parameter using a 10-fold cross- validation on the training set. Then, use ROC-AUC as a measure to tune in 10-fold Cross Validation I have a dataset split into two parts, TRAIN.csv (10000 obs.) and TEST.csv (60000 obs.) with 785 variables. I am unsure how to run glmnet(TRAIN, TRAIN_LABELS, family="binomial") -- because it is returning the error: Error in glmnet(TRAIN, TRAIN_LABELS, family = "binomial") : number of observations in y (785) not equal to the number of rows of x (60000) Let me know if there needs to be any clarifications. Thank you.