My dataset contains 13 attributes consisting of 10 Numerical and 3 Categorical attributes and Target. It has 180 observations
All Categorical are non-ordinal and each have the following categories:
It is a binary classification problem where we have to predict the probability for each class of the target class.
I have 3 Questions for above dataset:
Q1- For the categorical Feature, Should I use
df.get_dummies() or should I just combine custom label encoder with one-hot encoder?
Q2- Should scaling be done for
Numerical features only or it should be done for all the features including
categorical after encoding
Q3- What should be the best model to get the probability of the binary classification. So far, I have tried
log_loss score was
0.301 the best.