I need pointers to resources or parts of the video lectures where I can learn how to improve my tabular model.
For context I spent much of the weekend trying to build my very first ever model for this Kaggle competition just using what I’ve learnt from Lessons 1-4, and some parts of a Feature engineering book. I’ve also gone through different kernels and gotten some ideas about how to clean up data.
For this particular competition, so far I’ve managed to:
- Remove columns that are empty, mostly empty or contain just one unique feature
- re-sample the data so that I have a balanced sample of both classes
tabular_learnertrain the model with to up to 84.xx% accuracy.
So I’ve hit the limit of what I know. I’ve seen a few kernels about creating feature engineering and creating/generating new features from the dataset but I imagined that the databunch will handle these by itself.
Can someone please point me to what else I can do to improve the model.
(I imagine I will be able to get some more out of it if I learnt how to visualize and interpret the data better, and I’m already reading books on that, I just want to know if there’s anything else I can be doing with the fastai library)