one hot encoding classification-CodePudding

I am having a CSV file like this

F1  |  F2  |  F3  |  F4  |  Label

I used the get_dummies to change the label to a one-hot encoding representation, the data contains 3 different labels, so the file now looks like

F1  |  F2  |  F3  |  F4  |  Label1  |  Label2  |  Label3

let's say I want to use this data to train a machine learning model. I have to determine the features and label columns can I set it to:

Features, x = [0:3]
Labels, y = [4:6]

Is it right? I am thinking, by doing this way, maybe this could be understood as a multi-label problem since this is not! originally it was a multi-class classification.

Any help will be so much appreciated.

CodePudding user response：

You can try iloc or with filter

x = df.iloc[:, :4]
y = df.iloc[:, 4:]

# or

x = df.filter(like='F')
y = df.filter(like='Label')

print(x)

   F1  F2  F3  F4
0   1   2   3   4
1   1   2   3   4
2   1   2   3   4

print(y)

  Label1 Label2 Label3
0      x      y      z
1      x      y      z
2      x      y      z