I am new to Machine Learning and learned about Decision Tree Learning as part of a problem I am tackling on for an Undergraduate Research Assistant position.
The problem: From a set of positional data of hockey players and pucks, how can one recognize when a shot is made?
The issue: I have access to two games of hockey, one I will use to train my decision tree and the other I will use to test. However, for each game of hockey, we have about 80000 frames of data but only about 15 of them are annotated as shots made.
My question: Since we have a lot of negative frames but comparatively very little positive frames, will a decision tree model lead to overfitting? If yes, are there any alternate solutions?