I have a classification problem with the following example independent features:
|0.663||. 0.382||'yes', 'trump'|
The dependent variable is whether the comment is likely to receive a reply or not:
I want to apply regularisation to a the logistic regression model but I can't decide between L1 and L2.
I want to do this for three different datasets, one for online comments on sports articles, one for magazine and one for politics(national).
I then want to interpret the top e.g. 10 largest coefficients from these models. The following diagrams show this.
I am struggling to decide between the two models, and which would create a more interesting discussion. I understand the L2 diagram more, such that comments in the magazine with a number of recommendations is likely to receive a reply. So I'm favoring L2, but the diagram of L1 offers more interesting text words that appeared in the comments.
I aim to identify features that vary across the different news groupings, sports, politics, and magazine. To point out similarities or differences that could be of importance.