Constrain equation - Study guides, Class notes & Summaries

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eek 8 Variable Selection: - Important to limit the number of factors in the model for 2 reasons: o Overfitting – When the number of factors is close to or larger than the number of data points the model might fit too closely to random effects o Simplicity – on aggregate simple models are better than complex ones. Using less factors means that less data is required and the is a smaller chance of including insignificant factors. Interpretability is also crucial. Some factors are even ill...

Week 5 Notes Variable Selection what do we do with a lot of factors in our models? variable selection helps us choose the best factors for our models variable selection can work for any factor-based model - regression / classification why do we not want a lot of factors in our models? - overfitting: when the number of factors is close or larger than number of data points our model will overfit - overfitting: model captures the random effect of our data instead of the real effects too man...

Important to limit the number of factors in the model for 2 reasons: o Overfitting – When the number of factors is close to or larger than the number of data points the model might fit too closely to random effects o Simplicity – on aggregate simple models are better than complex ones. Using less factors means that less data is required and the is a smaller chance of including insignificant factors. Interpretability is also crucial. Some factors are even illegal to use such as race and...