Description
General Instruction
- I recommend you can write your answer using LA
- Submit uncompressed file(s) in the Dropbox folder via BeachBoard (Not email).
- Using scikit learn, evaluate the classification accuracy of the decision tree, bagging, AdaBoost, and Random forest.
- Find the py and use the ‘Gini’ index as the criterion.
- (5 points) 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.
- (10 points) 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.
- (5 points) 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.
- (15 points) 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.
- Submit your py and a report that includes the charts.
