Now we are ready to create a decision tree with all parameters set with default values. You may call your decision tree as "tree". Use DecisionTreeClassifier$(),$ and make sure set random$\_$state $=0.$

$[16]$

$0$s

from sklearn.tree import DecisionTreeClassifier

tree $=$ DecisionTreeClassifier$($random$\_$state$=0)$

tree.fit$($X$\_$train, y$\_$train$)$

Then we get the accuracy score on both training set and test set.

Do you think the model has the issue of overfitting? Why?