Isye 6501 - Midterm 1 Study Guide Graded A+

what does a smaller AIC encourage {Ans: higher likelihood and less parameters}How do we find the best value of k in k means? {Ans: Elbow method: we calculate the total distance of each data point to its cluster center and plot it in two dimensions. We look for the kik in the graph.}When is ARIMA better than exponential smoothing? {Ans: When the data is less stable}What are the data splits when working with one model? {Ans: 70-90% training, 10-30% test}A logistic regression model can be especially useful when the response... {Ans: ...is a probability (a number between zero and one) or is binary (either zero or one).}A common rule of thumb is to stop branching if a leaf would contain less than 5% of the data points. Why not keep branching and allow models to find very close fits to each very small subset of data? {Ans: Fitting to very small subsets of data will cause overfitting. With too few data points, the models will fit to random patterns as well as real ones}what is adjusted r-squared {Ans: same as r^2 but favors simpler models by penalizing for using too many variables}The farther