Whay did LASSO fail to perform feature selection？
Lasso did not fail to perform feature selection. It just determined that none of the 15 features were unimportant. For the one's where you get coefficients = 0.1 this just means that they are less important when compared to other more important features. So I would not be concerned!
Also 15 features is not a large amount of features for Lasso to determine the important one's. I mean it depends on the data so for some datasets, it can eliminate some features from a dataset of 10 features and sometimes it won't eliminate any from a dataset of 20. It just depends on the data!
Cheers!
I encountered a problem while doing my ML project. Hope to get some advice from you!
I fit logistic LASSO on a dataset with only 15 features trying to predict a binary outcome. I know that LASSO is supposed to do feature selection and eliminate the unimportant ones (coefficient = 0), but in my analysis, it has selected all the features and did not eliminate any one of them. My questions are:

Is this because I have too few features, or that the features are not correlated with each other(low co-linearity?)
Is this a bad thing or a good thing for a classification model?
some coefficients of the features LASSO selected are less than 0.1, can I interpret them as non-important or not that important to the model?

p.s. I run the model using the sklearn package in python.
Thank you!