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Binary Classification: Tips and Tricks from 10 Kaggle Competitions

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.


READ NEXT

F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation Metric Should You Choose?

9 mins read | Author Jakub Czakon | Updated July 13th, 2021

PR AUC and F1 Score are very robust evaluation metrics that work great for many classification problems but from my experience more commonly used metrics are Accuracy and ROC AUC. Are they better? Not really. As with the famous “AUC vs Accuracy” discussion: there are real benefits to using both. The big question is when

There are many questions that you may have right now:

  • When accuracy is a better evaluation metric than ROC AUC?
  • What is the F1 score good for?
  • What is PR Curve and how to actually use it?
  • If my problem is highly imbalanced should I use ROC AUC or PR AUC?

As always it depends, but understanding the trade-offs between different metrics is crucial when it comes to making the correct decision.

In this blog post I will:

  • Talk about some of the most common binary classification metrics like F1 score, ROC AUC, PR AUC, and Accuracy
  • Compare them using an example binary classification problem
  • tell you what you should consider when deciding to choose one metric over the other (F1 score vs ROC AUC).

Ok, let’s do this!

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