I am working on a binary classification problem where I have a mix of continuous and categorical variables.
Categorical variables were created by me using
get_dummies function in pandas.
Now my questions are,
1) I see that there is a parameter called
drop_first which usually is given the value
True. Why do we have to do this?
Let's say for the purpose of example, we have 2 values in gender column namely
Female. If I use
drop_first=True, it returns only one column. like
gender_male with binary
0 as values
For example, If my feature importance returns
gender_male as an important feature, Am I right to infer that it is only
Male gender that influences the outcome (because male is denoted as 1 and female is 0) and female (0's) don't impact the model outcome? or 0's in general doesn't play any role in ML model predictions?
2) Let's say my gender has 3 values for example
Transgender. In this case if I use
drop_first=True, it would only returns two columns
gender_male with 1 and 0 - Here
gender_female with 1 and 0 - Here
3) What's the disadvantage of not using
drop_first=True? Is it only about the increase in number of columns
Can you help me with the above queries?