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Overfitting in ML is when a model learns training data too well, failing on new data. Investors should avoid overfitting as it mirrors risks of betting on past stock performances. Techniques like ...

A key finding was that most AutoML tools tended to favor tree-based models and ensembles, which often delivered high accuracy but raised concerns about interpretability and overfitting. The study ...