Winning Solutions Overview: Kaggle Instacart Competition

Data Newsletter This short post is part of the data newsletter. Click here to sign up.

The Instacart "Market Basket Analysis" competition was about predicting which products in the next order would be a product that user had already ordered before.

1st Place (F1 0.4091)

User didn't publish his strategy.

2nd Place (F1: 0.4082) (LINK)

Approach: Manual Feature Engineering + XGBoost (Python)

Note: Optimizes F1-score directly (see references)

3rd Place (F1: 0.4081) (LINK)

Approach: Feature Learning with Neural Networks, then classification using Tensorflow and LightGBM(Python)

4th Place (F1: 0.4074) (LINK)

Approach: Manual Feature Engineering + Catboost

Note: Optimizes F1-score directly (see references)

5th Place (F1: 0.4073)

User didn't publish his strategy.

6th Place (F1: 0.4072) (LINK)

Approach: Manual Feature Engineering + CatBoost

Note: Optimizes F1-score directly (see references)

References

• CatBoost is a gradient boosting library with easier handling for categorical features.

• Queirozf.com: Evaluation Metrics for Classification Problems: Quick Examples + References

• Ye et al., 2012: Optimizing F-Measures: A Tale of Two Approaches

  • Here's a simple implementation in Python: F1-Expectation-Maximization

• Dembczynksi et al., 2011: An Exact Algorithm for F-Measure Maximization

• Solution summary instacart

  • More solutions and overviews here. Thanks to Kaggle user Lannister.