The goal is to build a decision tree that, based on the other features in the set, predicts whether or not a patient has malignant cancer. So this is a classification problem. Using tree.DecisionTreeClassifier and other functions in the scikit$-$learn library, one can build a decision tree and calculate both its training accuracy when fitted to the entire data set as well as its accuracy using $10-$fold cross validation $($which gives a better idea of true accuracy$).$ In this question you will need to complete two sub$-$components:

$($a$)$

$($a$)$ Make a plot visualizing the performance of a tree.DecisionTreeClassifier as you search for an optimal max$\_$depth parameter. Vary the depth of your decision tree using max depth $=1,2,...,10$ and record the results from the following evaluation procedures for each setting:

The accuracy when training and testing on the full dataset.

$10-$fold cross$-$validated accuracy.

Plot the results of both evaluation procedures on the same plot with evaluation scores on the y$-$axis and max depth values on the x$-$axis. Use $10$ as your random seed$/$state for the decision tree and the cross$-$validation. Use a legend to label both evaluation procedures.