yourquant_rick on Instagram: "In most machine learning fields, hyperparameter tuning is about maximizing performance on validation sets. Grid search, random search, or Bayesian optimization are applied until the “best” set of hyperparameters is found. But in financial ML, this approach is dangerous. Financial data is noisy, non-stationary, and limited. Aggressive hyperparameter tuning amplifies overfitting, tailoring the model too closely to historical quirks that won’t repeat. The result: strat

Applied Machine Learning, Part 3: Hyperparameter Optimization