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How he built the best Covid forecasting model, lessons learned and how to improve model performance with Youyang Gu - The Data Scientist Show#032
The Data Scientist Show
The Importance of Starting with Linear Models and Keeping Things Simple
Linear models should be the starting point in a modeling process./nHigher order nonlinear models should only be considered when linear models are not sufficient./nIn most cases, a linear model is sufficient for the task at hand./nThe performance of a linear model may be lower than that of a neural network within sample, but it tends to perform better out of sample./nNot everyone realizes the importance of evaluating models with out of sample data./nChoosing a simple model is crucial./nStarting with a single feature is recommended when building a code.
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