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