2 votes

Machine Learning Models for Longitudinal Data

I added "longitudinal variables" that take into account the number of times the students took the test and their most recent average cumulative score: My Question: a. Does the approach that ...
  • 101
2 votes

Distinguishing text with opposite meanings in SVM (False Information Detection)

Going step by step: Preprocessing Preprocessing is a big deal in NLP, out there you'll find many tutorials describing the classic steps but few explanations about why and when you should actually ...
2 votes

Identify features don't hold required information in a ML problem

To test hypothesis 1 the only way is to try different architectures and/or approaches. Basically brutal grid search. To test hypothesis 2 the way to go is data exploration and feature importance ...
1 vote

Machine Learning Models for Longitudinal Data

I think there are some things you can do to get it work better. Suggestions: add a column to the input giving number of previous tries at the test. If there was a score to go with it, include the ...
1 vote

Which models can I use for supervised learning with images?

Papers With Code have a great summary of the tasks in computer vision and their respective State of the Art models. Also the tasks page from HuggingFace serves as a great reference.

Only top scored, non community-wiki answers of a minimum length are eligible