quantopian · cfenaroli · Jul 10, 2017 · Jul 17, 2017 · Jul 25, 2017 · Aug 1, 2017
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+Given data with many different dimensions, deciding which ones to include is a non-trivial task. Including too many increases the risk of overfitting and multicollinearity while too few means your model might miss out on important relationships. The next important step, once you have selected your model, is to determine whether or not it is well-founded. This lecture provides a brief overview of the methods and criteria behind model selection and validation.
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		Given data with many different dimensions, deciding which ones to include is a non-trivial task. Including too many increases the risk of overfitting and multicollinearity while too few means your model might miss out on important relationships. The next important step, once you have selected your model, is to determine whether or not it is well-founded. This lecture provides a brief overview of the methods and criteria behind model selection and validation.