CODEWITHSUNDEEP
data-science
a zEnItH
a zEnItH

Reputation: 137

Binomial And Multinomial Classification in ML


I got a project in which my task is to build network intrusion detection system to detect anomolies and attacks in the network.
There are two problems.
1. Binomial Classification: Activity is normal or attack
2. Multinomial classification: Activity is normal or DOS or PROBE or R2L or U2R

But before this I get some confusion in these terms Binomial/Multinomial Classification. Help me to understand/ if possible please share a sort code... which gives me more help.

I tried to search these term on google/youtube but can't find proper definition with some code

I do only these thing with my code:-
 clean/transform/outlier detect/missing value treatment
 model_selection/accuracy test

so my next step is to make classification of Binomial/Multinomial Classification Thanks for help...

Upvotes: 1

Views: 158

Answers (1)

LaSul
LaSul

Reputation: 2411

First, do not hesitate to post on https://datascience.stackexchange.com/ for these kind of question that is more Data Science than coding issue.

Second, the answer is as simple as :

  • Binary (and not Binomial) Classification means only 2 targets to find.

    => In your case Normal vs Attack

  • Multilabel / Multiclass / Multinomial Classification means more than 2 targets to find.

    => Your case : Normal, DOS, PROBE, REL & E2R.

You can find example on https://scikit-learn.org/stable/supervised_learning.html#supervised-learning

Upvotes: 2

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