1. How logistic regression maps all outcome to either 0 or 1.
The equation for log-likelihood
function (LLF) is :
LLF = Σi( i log( ( i)) + (1 − i) log(1 − ( i))). y p x y p x
How logistic regression uses this in maximum likelihood
estimation?
2. We can apply PCA to reduce features in a data set for model
construction. But, why do we still
need regularization?
What is the difference between lasso and ridge...