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raunakdoesdev / march-madness

March madness data analysis for MIT 6.S079

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AutoML Leaderboard

Best model name model_type metric_type metric_value train_time
1_Default_LightGBM LightGBM logloss 0.612197 12.13
2_Default_Xgboost Xgboost logloss 0.606966 7.32
3_Default_CatBoost CatBoost logloss 0.602261 2.3
the best Ensemble Ensemble logloss 0.600326 0.7

AutoML Performance

AutoML Performance

AutoML Performance Boxplot

AutoML Performance Boxplot

Features Importance

features importance across models

Spearman Correlation of Models

models spearman correlation

About

March madness data analysis for MIT 6.S079

License:MIT License

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Language:Jupyter Notebook 99.5%Language:Python 0.5%