المدربMeta-labelling labels each event of a primary model with 1 if the trade in the primary model’s direction made money (for example, its triple-barrier return is positive) and 0 otherwise, and trains a secondary model on these labels to estimate the probability that the primary call is right; the probability is then used to filter or size the trade (López de Prado, 2018; Joubert, 2022).
ml_labels.meta.