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Machine Learning Advanced Pro

LightGBM

Gradient boosting internals and hyperparameter mastery

25 min read 16 views 1 enrolled

Understand how LightGBM builds sequential ensembles of trees, why leaf-wise growth outperforms level-wise, what every key hyperparameter controls, why a slow learning rate with more trees generalizes better, and how to diagnose and fix overfitting through regularization.

Practice questions (4)

  • Learning Rate vs Tree Count Trade-off

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  • Diagnosing Overfitting and Choosing Regularization Parameters

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  • min_child_samples as a Regularizer

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  • subsample vs colsample_bytree

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