Mmastodon TechnologySoftware first seen 2 d ago, last 2 d ago, peak #10
Student enters Kaggle competition as college machine learning project
Original: Today I applied to a kaggle competition, it's actually a college project. Training a ML model. Some fields are categoric
A student has entered a Kaggle competition as part of a college project, training a machine learning model on a dataset of over 100,000 rows with categorical fields. They are comparing three gradient boosting libraries — XGBoost, LightGBM and CatBoost — using F1 Macro as the evaluation metric, and report that XGBoost fell behind early.
Why now: People following machine learning discussions are interested in how popular boosting libraries compare on a real competition dataset.
Evidence
- Today I applied to a kaggle competition, it's actually a college project. Training a ML model. Some fields are categorical but the data set is large, above 100K. I choose to see who'll do better - XGBoost, LGBM or CatBoost. XGBoot lost immediately, I used F1 Macro as my… · hackaday@www.urbanmind.net · 3
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