Implementing single node decision tree, bagging, adaboost using matlab

[login to view URL] a single-node decision tree: Write functions which take a data set and compute the optimal decision plane. The input set of instances can be of two or more dimensions. The output from the main function must be the identified projection vector.   [login to view URL] bagging and AdaBoost: Write functions which take a set of examples and identify the decision plane to separate the examples.  The input set of instances can be of two or more dimensions. The output from the main function must be the identified hyper plane.   [login to view URL], write functions to visualize the synthetic data and evaluate the methods with synthetic data using MATLAB. The functions should be able to allow user to specify which dimensions (no more than 3 dimensions) to be included in the visualization and be able to use color to differentiate examples from different classes. Conduct cross-validation and report the average accuracy, sensitivity, and specificity.