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This is a nice guide, I've also enjoyed playing around with the Ipython Notebook here: http://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks
by cstuder 11y ago
This is a nice guide, I've also enjoyed playing around with the Ipython Notebook here: http://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb http://nbviewer.ipython.org/github/justmarkham/DAT4/blob/mas...
The one question which remains: Is there a more intuitive guide which helps you with deciding which features you need to choose in order to get a good regression?
- alexhwoods 11y agoHey, one easy way to decide features is to use a correlation matrix. The stronger the correlation coefficient r is, the more of a linear relationship exists. The code goes like this - install.packages('corrplot') library(corrplot) mcor <- cor(crime) # if crime is your dataframe corrplot(mcor) That's one easy way to start out. Perhaps I'll write a post on feature engineering.
- cstuder 11y agoThank you, I will try this. And I would love to see such a post.