Skip to main content
Cross Validated

Return to Question

Bumped by Community user
added 6 characters in body; edited tags
Source Link
kjetil b halvorsen ♦
kjetil b halvorsen ♦
  • 82.8k
  • 32
  • 201
  • 663

I'm building a ridge regression model in scikit-learn and trying to find the optimal degree polynomial to use. The data I'm working with is a fairly predictable time series of hourly traffic volumes, and I'm predicting said volumes from the date, hour, and day of the week. R-squared values increase for both my train and test sets as I generate higher degree polynomial features, but suddenly drop from .91 to -1.4 when I go from degree 8 to 9, signifying that the 9th-order model is worse than the 0-order model. 

Any idea why this happens?

I'm building a ridge regression model in scikit-learn and trying to find the optimal degree polynomial to use. The data I'm working with is a fairly predictable time series of hourly traffic volumes, and I'm predicting said volumes from the date, hour, and day of the week. R-squared values increase for both my train and test sets as I generate higher degree polynomial features, but suddenly drop from .91 to -1.4 when I go from degree 8 to 9, signifying that the 9th-order model is worse than the 0-order model. Any idea why this happens?

I'm building a ridge regression model in scikit-learn and trying to find the optimal degree polynomial to use. The data I'm working with is a fairly predictable time series of hourly traffic volumes, and I'm predicting said volumes from the date, hour, and day of the week. R-squared values increase for both my train and test sets as I generate higher degree polynomial features, but suddenly drop from .91 to -1.4 when I go from degree 8 to 9, signifying that the 9th-order model is worse than the 0-order model. 

Any idea why this happens?

Bumped by Community user
Bumped by Community user
Source Link
Kevin Zhang
Kevin Zhang
  • 51
  • 1
  • 5

Why is my high degree polynomial regression model suddenly unfit for the data?

I'm building a ridge regression model in scikit-learn and trying to find the optimal degree polynomial to use. The data I'm working with is a fairly predictable time series of hourly traffic volumes, and I'm predicting said volumes from the date, hour, and day of the week. R-squared values increase for both my train and test sets as I generate higher degree polynomial features, but suddenly drop from .91 to -1.4 when I go from degree 8 to 9, signifying that the 9th-order model is worse than the 0-order model. Any idea why this happens?