#CurrencyPairPrediction Stacking (Stacked Generalization) • Train multiple models (level-0 models) independently. • Use a meta-model (level-1 model) to combine their predictions. Process: 1. Train Model A, Model B, Model C separately (maybe one tree, one SVM, one neural net). 2. Collect their predictions. 3. Train a new model (e.g., logistic regression) on these predictions to make final decisions. Stacking Learns: • How much to trust each base model under different conditions. Stacking is super powerful for Forex because different models perform better in different regimes (trending, volatile, news-driven).