C1_W3_Lab06_Gradient_Descent_Soln

and

C1_W3_Lab07_Scikit_Learn_Soln

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?

Thanks!