Ada Boost with boosting stumps

Implement an AdaBoost with boosting stumps (30 points) and apply the algorithm to the breast cancer data set [login to view URL], ID 322391. The data set contains 30 features and asks for a classification of breast tumors into benign and malignant. Plot the classification errors over the iterations and use 5-fold cross-validation to validate your results (70 points). Hint: you need to calculate the average cross-validation errors minus and plus one standard deviation. Decision stumps are often used as components (called ”weak learners” or ”base learners”) in machine learning ensemble techniques such as bagging and boosting. A boosting stump is a machine learning model consisting of a one-level decision tree. That is, it is a decision tree with one internal node (the root) which is immediately connected to the terminal nodes (its leaves). A decision stump makes a prediction based on the value of just a single input feature. Question 2. (100 points) Implement a Bagging with bagging stumps (30 points) and apply the algorithm to the breast cancer data set [login to view URL] Plot the average bootstrap errors plus or minus one standard deviation and compared with those in Adaboost procedure