Machine Learning in Trading Strategy Design › Key Parameters
Michael BrenndoerferJanuary 3, 20261 min read
Quantitative FinanceAll chapters Part 13 of 15Machine Learning and Alternative Data
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The key parameters for the supervised learning and regime detection models are:
- n_estimators: The number of trees in the Random Forest or Gradient Boosting model. More trees generally improve stability but increase training time.
- max_depth: The maximum depth of each tree. Constraining depth helps prevent overfitting to noise in financial data.
- learning_rate: Determines the contribution of each tree in Gradient Boosting. Lower rates with more trees often yield better generalization.
- n_components: The number of regimes (clusters) in the Gaussian Mixture Model. This defines how many distinct market states the model attempts to identify.