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ISYE 6501 - Midterm 2 2022

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when might overfitting occurs - when the # of factors are close to or larger than the # of data points causing the model to potentially fit too closely to random effects Why are simple models bette ... r than complex ones - less data is required; less chance of insignificant factors and easier to interpret what is forward selection - we select the best new factor and see if it's good enough (R^2, AIC, or p-value) add it to our model and fit the model with the current set of factors. Then at the end we remove factors that are lower than a certain threshold what is backward elimination - we start with all factors and find the worst on a supplied threshold (p = 0.15). If it is worse we remove it and start the process over. We do that until we have the number of factors that we want and then we move the factors lower than a second threshold (p = .05) and fit the model with all set of factors what is stepwise regression - it is a combination of forward selection and backward elimination. We can either start with all factors or no factors and at each step we remove or add a factor. As we go through the procedure after adding each new factor and at the end we eliminate right away factors that no longer appear. what type of algorithms are stepwise selection? - Greedy algorithms - at each step they take one thing that looks best what is LASSO - a variable selection method where the coefficients are determined by both minimizing the squared error and the sum of their absolute value not being over a certain threshold How do you choose t in LASSO - use the lasso approach with different values of t and see which gives the best trade off why do we have to scale the data for LASSO -if we don't the measure of the data will artificially affect how big the coefficients need to be [Show More]

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