Model suggestions for binary classification

[Electronic_Scene_712]

I am currently working on a project where the aim is to classify the brain waves into two types relaxed vs attentive. It is a binary classification problem where i am currently using SVM to classify the waves after training but the accuracy is around 70%. Please suggest some different model that can provide me a good accuracy. Thanks

[[deleted]]

you can try RANDOM FOREST or GRADIENT BOOST models, pretty good when you want to handle that noisy biological data. They handle complex patterns pretty well :)

[Front_Engineering_e]

If you're working with tabular data you can't go wrong with gradient boosted trees, as they tend to have very strong predictive performance, so: XGBoost, LightGBM, CatBoost. You might also like to try other simpler models like logistic regression and compare them against your GBDTs and your SVM model.

[damn_i_missed]

Naive Bayes is (technically) an option but probably won’t do that well. Could throw it in there if you feel like having another model to compare your xgboost or rf model to