Problem: Load the German Credit Data sample dataset from the UCI Machine Learning Repository (german.data-numeric) into R using a dataframe (Note: The final column is the class variable coded as 1 or 2). Use the caret package to perform a 80/20 test-train split (via the createDataPartition function), and obtain a training fit for a logistic model via the glm package. (Hint: You may select a subset of the predictors based on exploratory analysis, or use all predictors for simplicity.). What are the training Precision/Recall and F1 results? Next, use the trainControl and train functions to perform a k=10 fold cross-validation fit of the same model, and obtain cross-validated training Precision/Recall and F1 values. How do these values compare to the original fit? How does the performance on the test set for the original and cross-validated model compare?