[s5e6] Keep It One Hundred Now

Creating interaction terms between the top 3 features yielded a +0.002 boost in CV score.

The target is a top 5% finish! It’s all about those marginal gains and robust validation.

Using Optuna for automated hyperparameter tuning. 💡 Key Insights So Far [S5E6] Keep it One Hundred

I’m currently diving into the latest ! The challenge is all about refining models to push the limits of performance. Here’s a breakdown of my current workflow and some key takeaways: 🛠️ The Tech Stack Models: Testing a blend of XGBoost, LightGBM, and CatBoost.

As noted by top competitors like Chris Deotte , retraining the final ensemble on the full dataset with a fixed iteration count (avg early stopping + 25%) is proving crucial for the leaderboard. Creating interaction terms between the top 3 features

Stick to a 5-fold or 10-fold Stratified CV to ensure the model isn't just chasing noise.

#Kaggle #MachineLearning #DataScience #XGBoost #Python #PlaygroundSeries #KeepItOneHundred If you'd like, let me know: Your or target Which model you're leaning toward (XGBoost, CatBoost, etc.) Using Optuna for automated hyperparameter tuning

The provided phrase "[S5E6] Keep it One Hundred" likely refers to the competition, which focuses on a machine learning task related to the "Keep it One Hundred" theme (often involving achieving high accuracy or working with a specific dataset).