I have a model that, given a headline, predicts if the story will get >10 votes. It’s a terrible model, for a few reasons. The most fundamental is that if the same article is submitted 10 times it could get wildly different scores, that’s the way it does. The tail end of the model [1] is logistic regression because it deals gracefully with this kind of situation. I wish I knew how to treat this as a regression problem (predict the score), there is probably a better loss function than what I use, but when I treat it at as a regression problem I get an even worse model.
The highest score this model ever gives is 70% for something like “Richard Stallman is dead”
I have another model that predicts If the comment/score ratio > 0.5 which is about the average for the site. This is a much better model, close to the first recommender models I made. Trained on articles with score > 10 the input is less noisy for one thing. It’s how a learned y’all like to talk about cars.
I have a model that, given a headline, predicts if the story will get >10 votes. It’s a terrible model, for a few reasons. The most fundamental is that if the same article is submitted 10 times it could get wildly different scores, that’s the way it does. The tail end of the model [1] is logistic regression because it deals gracefully with this kind of situation. I wish I knew how to treat this as a regression problem (predict the score), there is probably a better loss function than what I use, but when I treat it at as a regression problem I get an even worse model.
The highest score this model ever gives is 70% for something like “Richard Stallman is dead”
I have another model that predicts If the comment/score ratio > 0.5 which is about the average for the site. This is a much better model, close to the first recommender models I made. Trained on articles with score > 10 the input is less noisy for one thing. It’s how a learned y’all like to talk about cars.
[1] what attention folks call the “head”