RT Journal Article SR Electronic T1 Meta-Labeling: Theory and Framework JF The Journal of Financial Data Science FD Institutional Investor Journals SP 31 OP 44 DO 10.3905/jfds.2022.1.098 VO 4 IS 3 A1 Jacques Francois Joubert YR 2022 UL https://pm-research.com/content/4/3/31.abstract AB Meta-labeling is a machine learning (ML) layer that sits on top of a base primary strategy to help size positions, filter out false-positive signals, and improve metrics such as the Sharpe ratio and maximum drawdown. This article consolidates the knowledge of several publications into a single work, providing practitioners with a clear framework to support the application of meta-labeling to investment strategies. The relationships between binary classification metrics and strategy performance are explained, alongside answers to many frequently asked questions regarding the technique. The author also deconstructs meta-labeling into three components, using a controlled experiment to show how each component helps to improve strategy metrics and what types of features should be considered in the model specification phase.