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This lecture covers supervised learning in financial econometrics, focusing on linear regression and model fitting. Topics include the assumptions of linear models, model training, residuals, and potential problems like non-linearity, correlation of error terms, and heteroskedasticity. It also discusses basis functions, the bias-variance trade-off, subset selection methods, cross-validation, regularization techniques like ridge regression and Lasso, and the concept of the random forest algorithm.
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