Description
Complete the following tasks:
- Implement three classification algorithms by yourself: Nearest Neighbor, Decision Tree, and Naïve Bayes.
- Implement Random Forests based on your own implementation of Decision Tree.
- Adopt 10-fold Cross Validation to evaluate the performance of all methods on the provided two datasets in terms of Accuracy, Precision, Recall, and F-1 measure.
We will send you an invite for a Kaggle competition. For that dataset, we hold out the class labels for testing data. Apply various tricks on top of any classification algorithm discussed in class (including nearest neighbor, decision tree, Naïve Bayes, SVM, logistic regression, bagging, AdaBoost, random forests) and tune parameters using training data. You can call packages for these algorithms but need to implement any improvement on top of these algorithms. Submit your classification result for the testing data. Your efforts towards improving these algorithms will be evaluated. Those who are among the top on the leaderboard after the deadline will receive bonus points
