For Decision Tree:

$(3$ points$)$ Based on the output from tree$\_$clf$.$predict$\_$proba and tree$\_$clf$.$predict, explain the predicting results. $($How do you decide which category an instance belongs to$?)$

$(3$ points$)$ Were the predictions for these instances correct? Why?

$(3$ points$)$ Modify the DecisionTreeClassifier model with the following hyperparameter settings:

maximum depth: $3$

measure with "entropy" instead of "Gini impurity"

For Random Forest:

$(2$ points$)$ Split the original dataset into training set $(75\%)$ and test set $(25\%).$

$(2$ points$)$ Train the random forest with the training set.

$(2$ points$)$ Apply the trained classifier to the test data.

$(2$ points$)$ Predict the category of the following instance $($print out the category name$)$:

sepal length $=6\mathrm{.}1,$ sepal width $=2\mathrm{.}6,$ petal length $=1\mathrm{.}5,$ petal width $=0\mathrm{.}2$

$(2$ points$)$ Print the score and the confusion matrix of the classifier.

Note: You can directly update the code for coding questions. You need to answer the remaining questions in a Markdown field.

$(1$ points$)$ Create a new Markdown field at the end of this file and put your answers in this field. Submit this file to the Blackboard.