# Ranking

In this lesson, we'll explore different modeling options for the Tweet ranking problem.

In the previous lesson, you generated the training data. Now, the task at hand is to predict the probability of different engagement actions for a given Tweet. So, essentially, your models are going to predict the probability of *likes, comments and retweets*, i.e., *P(click), P(comments) and P(retweet)*. Looking at the training data, we know that this is a classification problem where we want to minimize the classification error or maximize area under the curve(AUC).

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