changy12
commented
8 years ago Dear ANGELA6XU: Thank you for your sincere advice.
We used mutual information instead of correlation, since all the variables here are categorical.
You mean we can introduce each variables more specifically? The detailed information are shown in the table.
Yes, Naive Bayes is a rough method.
However, this mid-term project requires only preliminary analysis, so we only ran a baseline model and left more work on November. Otherwise, the project is close to due.
In addition, we found our original goal does not make sense on Oct. 28th, so we changed our goal and had very limited time to restart. Since Naive Bayes algorithm is one of the most simple algorithm for classification problem that directly use categorical features without one-hot encoding, we adopted it for the start. Certainly, we will adopt and compare other classification methods during November.
Thank you. Best, Ziyi
The goal of this project is to predict whether an inpatient is likely to survive. The team used naïve bayes classification method to make prediction. In general, the team shows a deep and sophisticated understanding about the algorithm. Also, it has clear and detailed plan for further development.
I am confused as to what the plots are in the report. Also, it would be great if you would include any plots for the correlation of the data set, demonstrating any relationships among these variables. For the data preprocessing part, the description of the original data could be more specific, which can be helpful for understanding feature selections.
After feature selection, the team can demonstrate more on model selection. For example, explain why you choose naïve bayes classification instead of other classification models. The advantages and disadvantages between naïve bayes and other classifiers, etc.