Create a confusion matrix and report the results of your model for the train set. Interpret and discuss the confusion matrix. Which misclassifications are more damaging for the analysis, False Positives or False Negatives?
Use the College dataset from the ISLR library to build a logistic regression model to predict
whether a university is private or public.
1. Import the dataset and perform Exploratory Data Analysis by using descriptive statistics
and plots to describe the dataset.
2. Split the data into a train and test set – refer to the Feature_Selection_R.pdf document for
information on how to split a dataset.
3. Use the glm() function in the ‘stats’ package to fit a logistic regression model to the
training set using at least two predictors.
4. Create a confusion matrix and report the results of your model for the train set. Interpret and discuss the confusion matrix. Which misclassifications are more damaging for the analysis, False Positives or False Negatives?
5. Report and interpret metrics for Accuracy, Precision, Recall, and Specificity.
6. Create a confusion matrix and report the results of your model for the test set.
7. Plot and interpret the ROC curve.
8. Calculate and interpret the AUC
DETAILED ASSIGNMENT
