Bagging Mission: Write Python3 code to implement the bagging algorithm. Data set: Wine Data Set Read the data set description. Use the data set to implement the bagging algorithm. Randomly select 20 instances as the test data and the rest as training data (Keep these 20 test instances so that TA can reproduce your results). • Approaches: Base learner (required): decision trees. Please use scikit learn library: sklearn.tree. Decision Tree Classifier. Bagging (required): Please use scikit-learn library: sklearn.ensemble. BaggingClassifier Other data pre-processing or feature engineering methods (optional): You can apply any technique you prefer. • Performance metric: Successfully built the bagging algorithm using the training data and tell us the accuracy for the test data.

Bagging Mission: Write Python3 code to implement the bagging algorithm. Data set: Wine Data Set Read the data set description. Use the data set to implement the bagging algorithm. Randomly select 20 instances as the test data and the rest as training data (Keep these 20 test instances so that TA can reproduce your results). • Approaches: Base learner (required): decision trees. Please use scikit learn library: sklearn.tree. Decision Tree Classifier. Bagging (required): Please use scikit-learn library: sklearn.ensemble. BaggingClassifier Other data pre-processing or feature engineering methods (optional): You can apply any technique you prefer. • Performance metric: Successfully built the bagging algorithm using the training data and tell us the accuracy for the test data.