Hi everyone. Can anybody answer to my question?

Is the Simple Logistic Regression which is discussed in the 3rd part in this notebook(week3) ,written by one line code —>>>

clf = sklearn.linear_model.LogisticRegressionCV()

is the same as Logistic Regression with a Neural Network mindset (week 2 notebook)

Or in the Simple logistic regression we do not use any activation??

Is there any differences between this 2 regressions?

Hi, the implementation of the simple logistic regression in week 2 notebook is done with neural networks, however, the `LogisticRegressionCV`

algorithm of `sklearn`

relies on a different mathematical approach which do not need any activation function.

You can see the source code of `LogisticRegressionCV`

here. In the class definition they’ve included the following:

This class implements logistic regression using liblinear, newton-cg, sag of lbfgs optimizer