SKLearn Logistic Regression output VERY different weights than manual Gradient Descent imp


has the same X_train and y_train set

But for SKLearn version, if we output
print(“weights:”, lr_model.coef_)

We will see the weights are very different from manual impl version.
– lab06 output:
Iteration 0: Cost 0.684610468560574
Iteration 1000: Cost 0.1590977666870456
Iteration 2000: Cost 0.08460064176930081
Iteration 3000: Cost 0.05705327279402531
Iteration 4000: Cost 0.042907594216820076
Iteration 5000: Cost 0.034338477298845684
Iteration 6000: Cost 0.028603798022120097
Iteration 7000: Cost 0.024501569608793
Iteration 8000: Cost 0.02142370332569295
Iteration 9000: Cost 0.019030137124109114

updated parameters: w:[5.28 5.08], b:-14.222409982019837

 Lab07 output for the same data
 weights: [[0.90411349 0.73587543]]  

Do we know why? Or did SKLearn internally doing some feature scaling already?
And also, how to output the ‘b’ from SKLearn result?


Hi Lizhang,

I think we can’t explain it without diving into the implementation. Some notes here:

  1. sklearn enables regularization for logistic regression by default. You need to turn it off yourself. The lab doesn’t have regularization.
  2. sklearn uses tol as one of ways to stop training. The lab doesn’t have that, but only number of iterations.
  3. sklearn offers a few solvers, and if you try them one by one, each of them will give you a different set of weights, that’s a hint that their implementations could be different from ours

You can find about regularization setting, choices of solvers, training stopping criteria, and how to print the b (which is called intercept) in sklearn doc.


Lizhang, if you want to further investigate this, please make sure the weights are converged, before comparing them.

Very helpful. Thanks for the detailed reply!