Linear Regression

Question 1 2 pts This machine learning algorithm stores training data for future reference. Therefore, when deployed, the algorithm makes predictions for new data input by merely comparing the stored data samples which are closer to it. The prediction will follow the majority vote for classification or weighted average of neighboring samples for regression. This kind of approach is very flexible as it discards any assumption on the model but it may slow and can overfit to the selected training data samples. What do you categorize the kind of model? O parametric model nonparametric model online learning O batch learning Question 4 1 pts What Linear Regression training algorithm can you use if you have a training set with billions of data samples? Question 5 2 pts Select all that describe the pros of online learning. It works well for the problem that changes over time. It is usually faster than batch learning. The convergence conditions are well-understood. It is easy to examine theoretically