Ordinary Least Squares Compute Time
linear_model.LinearRegression() documentation lists:
From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) wrapped as a predictor object.
Why might scikit-learn’s implementation seem much faster than calling
linalg.lstsq() on its own?
It appears scikit will cache results to precalculate features that haven’t changed. If you use random data instead with
The results are more comparable.