Final Peer Review_hc936

[HanyinCao]

This project is impressive. Medical cost has been discussed more and more frequently, and how to reduce that cost becomes an important problem. In details, this project has several merits. Firstly, the dataset is messy, containing various types of data and some outliers. This team utilized some typical feature engineering methods such as one hot encoding, properly involved the categorical variables into the model. Secondly, the innovative method of measuring error really impresses me. However, although this method can properly measure the accuracy of the model, I think the comparison between this new method and traditional methods is necessary. Thirdly, the use of quantile regression pushed the project one step further. This part of research tells us that the influence of features on different quantile of cost is different, giving us new insight on how to solve the initial problem: finding proper ways to cut down the medical cost.

This project also has some drawbacks. Firstly, we study many types of loss functions and regularizers in the class. I think more loss functions, regularizers and their combination worth to be tried. Secondly, the correlation between some variables may be high. In this case, we can drop some of the features. Thirdly, this team can analyze the reason why random forest gives us the best result while the result of L2 regression is not satisfying. This kind of analysis can help us better understand the pros and cons of different models.