Kaggle Grandmaster Panel - H2O AI World London 2018

Name: Kaggle Grandmaster Panel - H2O AI World London 2018
Uploaded: 2018-11-02
Duration: 46 min
Description: Kaggle Grandmasters answer questions at H2O World London 2018: validation before modeling, hours per week, favorite tools, and how to spot leakage.

Generative AI
H2O Driverless AI

Overview

A panel of Kaggle Grandmasters, several of them H2O.ai data scientists, fields audience questions at H2O World London 2018 on how they actually work competitions: setting up validation before touching a model, how many hours a week they put in, favorite tools, and how to spot leakage. The conversation also covers whether Kaggle skills transfer to real production work and how panelists use Driverless AI alongside manual feature engineering.

Key takeaways

Why nearly every panelist starts a competition by building a solid cross-validation setup before any serious modeling, and why a validation score that doesn't track the leaderboard is the first red flag
How much time top Kagglers actually spend, ranging from a couple hours a week to eight-to-ten hours a day during an active competition
Favorite tools across the panel: XGBoost, LightGBM, PyTorch, Keras/TensorFlow, and H2O's own Datatable for fast feature engineering
Why data leakage found on Kaggle mirrors leakage that shows up in real production models, and why learning to spot it is a transferable skill
How panelists use Driverless AI to get a fast baseline and feature-importance read before manually engineering features, while noting it can't be used for final Kaggle submissions

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