#SHARE R CODES

This question should be answered using the "Wine Quality" https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/ (winequality-white.csv) data set. A description of the data set can be found on the link provided. The objective of this question is to fit a regression tree model to predict the quality of the wine.

1. Produce some numerical and graphical summaries of the data set. Explain the relationships.

2. Make training set with 80% of the observations, and a testing set containing the remaining 20%.

3. Fit a regression tree with _quality_ as the response variable using the training set. Plot the tree and interpret the results. What test MSE do you obtain?

4. Use cross-validation in order to determine the optimal level of tree complexity. Does pruning the tree improve the test MSE?

5. Use random forests to analyze this data. What test MSE do you obtain?

6. Use the _importance()_ function to determine which variables are most important.