Soft stacking is a technique used in machine learning, particularly in predictive tasks, involving the combination of predictions from multiple models to achieve better accuracy than a single model. Unlike hard stacking, where predictions are binary or categorical, soft stacking utilizes probabilities or continuous values generated by base models. The process involves training first-level models (e.g., decision trees, neural networks), whose outputs are then used as input features for a second-level model (meta-model), such as logistic regression. Ensuring diversity among base models is key, as it increases generalization and reduces the risk of overfitting. Soft stacking requires careful hyperparameter tuning and cross-validation to avoid overfitting. This technique is popular in data science competitions, such as Kaggle, due to its effectiveness in improving prediction outcomes. #SoftStacking