1). What is overfitting? / Please briefly describe what is bias vs. variance. 2). How do you overcome overfitting? Please list 3-5 practical experience. / What is 'Dimension Curse'? How to prevent? 3). Please briefly describe the Random Forest classifier. How did it work? Any pros and cons in practical implementation? 4). Please describe the difference between GBM tree model and Random Forest. 5). What is SVM? what parameters you will need to tune during model training? How is different kernel changing the classification result? 6). Briefly rephrase PCA in your own way. How does it work? And tell some goods and bads about it. 7). Why doesn't logistic regression use R^2? 8). When will you use L1 regularization compared to L2?
