$12\mathrm{.}8$ LAB: Using the DecisionTreeClassifier$()$ on the NBA data Construct a decision tree to classify whether an NBA team won or lost a game based on the team's points in the game, elo score entering the game, and win equivalence. Read nbaallelo$\_$log$.$csv into a data frame. Subset the data containing pts$,$ elo$\_$i$,$ and win$\_$equivalent. Subset the data containing the labels, which are in the feature game$\_$result. Standardize the data. Split the data into $75\%$ train and $25\%$ test sets. Use DecisionTreeClassifier$()$ to initialize a classification tree using the train data. Use the classification tree to predict the results for the test data. Construct and print the confusion matrix. Ex: If only pts and elo$\_$i are used, the output is: