Applied Machine Learning 2019 - Lecture 06 - Linear Models for Regression

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Imputation, polynomial features
Ordinary Least Squares, Ridge Regression, Lasso, Elastic Net

  1. Andreas Mueller
  2. lasso
  3. ridge
  4. imputation
  5. missing values
  6. scikit-learn
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Комментарии
Автор

With the iterative imputation mentioned around 22:00 ~ 23:00. Does this mean you first impute the missing values with a simple imputation method like mean/median. Then use a regression model such as a random forest regressor to replace the previously imputed value again?

charlielu
Автор

Confused at 1:12:33 where you plotted the non-zero coefficient values for the Lasso model. You previously replied to a student's question that the coefficients from a Lasso model could never be zero? Were the coefficients simply rounded down?

charlielu