The goal is to predict click through rate of article content. Currently, the linear regression is used and the input data set is at article level. The label is the click rate of the article. The issue is the sample size is too small.
I am thinking to use logistic regression instead and use input data at client level. the label will be if the article is clicked or not. However, the issue is the class is very unbalanced (around 1:100). The good thing is the sample size is a lot larger and most of the data set I saw online is at client level.
Is it better to switching to logistic regression?