. Using scikit learn, evaluate the classification accuracy of the decision tree, bagging, AdaBoost, and Random forest.
(a) Find the Assn9.py and use the ‘Gini’ index as the criterion.
(b) Complete the method decision tree that generate a decision tree from X train, y train and predict y from X test. This method should record its prediction accuracy at tree score.
(c) Similarly, complete the method bagging that generate multiple decision trees using the bagging. This method should record its prediction accuracy at bagging score by varying the parameter n estimators. Draw a chart whose Xaxis is n estimators and Y-axis bagging score, and the chart should have more than 20 data points of different X-axis values.
(d) Similarly, complete the method boost that generate multiple decision trees using the AdaBoost. Draw a chart whose X-axis is n estimators and Yaxis boost score, and the chart should have more than 20 data points of different X-axis values.
(e) Similarly, complete the method forest that generate multiple decision trees using the random forest. Draw a chart whose X-axis is n estimators, Y-axis max features, and Z-axis forest score. The chart should have more than 100 data points of different pair of X-axis and Y-axis values.
