Feature Importance for Regression Models

[victor_palacios]

It looks like the data you provided still needs to go through linear regression preprocessing (checking for redundant variables, removing highly correlated variables, checking that you have enough unique samples per group vs the number of variables you have).

In particular I found a loop where all the variables you provided had the exact same input which causes the ERROR Linear Regression Learner 3:36 Execute failed: 0 is smaller than, or equal to, the minimum (0)

This issue was also seen for other members and the answer reported there applies to your case as well:

time series prediction with linear regression

I guess if you are wanting to predict only on inside values, you could take the lag of your temp (not the in/out value) and use that as a feature instead? (The reason you are currently seeing the error is because you are predicting using a variable that only has only one class - In. Even when you lag that variable, it never changes. So there is nothing the model can predict with.)

One thing I find curious though is that you’re using Linear Regression with string variables. In such a case, why not try tree regression instead?