cst 383: learning log 7

This week, was mainly focused on finals prep and also studying for the quiz. The focus was Logistic Regression. It is mainly used for predicting categories. This is a good way to predict inside of a standard 0 -> 1 range, since linear models don't do this. The Sigmoid function can dissect the output in the 0 -> 1 range. Additionally, we also covered underfitting and overfitting, and looking at learning curves which tests errors against various sizes of datasets. I learned that if there is a huge gap in between the two curves, there's an issue with variance, and we may need more data or just a simpler model. And if both of the errors are high and near each other, there could be a need for a more flexible model. It really does depend on the outputs. Finally I learned about translating data into a format that is actually processable. Personally I also did some research on the difference between accuracy and roc auc, because I learned that accuracy just focuses on a percentage of accuracy, rather than showing performance across the entire data set. The area under the curve is what summarizes the data plot. It also supports an inbalanced dataset, which is why I felt it was the best option for our Churn homework activity.