nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

765750_en-US

liliconcom

Best approach and alternative to simple logistic regression to model the probability of belonging to a group

Hello

I need a Little help on picking the right approach to solve my pronlem.

I have a continuous variable X than can take any value between [-45 0].

I have a dataset of X values for 12 distinct groups, see below their histogram.

I'm trying to build two models:

1) A model that, for a given new value of X gives me the probability of belonging to each goup and gives me the most likely group.

2) A model that, for A new entry of X that is suppose to belong to a given group, give me the probablity of this entry not being for that said group.

I initially thaught logistic regression was the best approach, at least for 1) but for 2) I'm not sure. In particular for group with very narrow distribution like K for instance.

Thanks in advance!

Advanced Statistical Modeling

Basic Data Analysis and Modeling

Comments

There are no comments yet