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I am currently running a linear regression on my time-series data set. However, depending on which python module I use, I get completely different results. First I used Sklearn, and my model had an R^2 score of about 0.65. After that I tried using statsmodels.api, to get the summary of the regression, since Sklearn doesn't provide one, and I got a completely different R-2 score of 0.96.

After this, I used the linear model of statsmodels.formula.api and got another different result, this time, closer to my first result. (R^2 of 0.65)

I want to know why this happens. It seems like a mistake on my part, but I am pretty sure I am using the same data for all of the regressions (doing converting of the data frame to np.arrays where necessary). Can such large differences happen because of differences in implementation of the module?

Thank you for taking the time to read this.

Traykuh
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    Possible duplicate of https://stackoverflow.com/q/54614157/9987623 – AlexK Apr 15 '19 at 06:33
  • There is also Q&A on the same topic on the Statistics site: https://stats.stackexchange.com/questions/249892/wildly-different-r2-between-statsmodels-linear-regression-and-sklearn-linear?rq=1 – AlexK Apr 15 '19 at 06:36
  • for the difference in definition of Rsquared when a constant is included or not, see for example https://stackoverflow.com/questions/29664471/why-would-r-squared-decrease-when-i-add-an-exogenous-variable-in-ols-using-pytho – Josef Apr 15 '19 at 18:26

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