Binary Classification: Tips and Tricks from 10 Kaggle Competitions

Posted August 12, 2020

Imagine if you could get all the tips and tricks you need to tackle a binary classification problem on Kaggle or anywhere else. I have gone over 10 Kaggle competitions including:

– and pulled out that information for you.

Dive in.

Modeling

Dealing with imbalance problems

Metrics

Loss

BCE and Dice Based

Focal Loss Based

Custom Losses

Others

Cross-validation + proper evaluation

Post-processing

Ensembling

Averaging 

Averaging over multiple seeds

Geometric mean

Average different models

Stacking

Blending 

Others

Repositories and open solutions

Repos with open source solutions

Image based solutions

Tabular based solutions 

Text classification based solutions

Final thoughts

Hopefully, this article gave you some background into binary classification tips and tricks, as well as, some tools and frameworks that you can use to start competing.

We’ve covered tips on:

  • architectures,
  • losses,
  • post-processing,
  • ensembling,
  • tools and frameworks.

If you want to go deeper, simply follow the links and see how the best binary classification models are built.

Data Scientist