Stacking in AI is an advanced ensemble learning technique that combines multiple machine learning models to improve overall prediction performance. It operates by training several base models (level-0 models) on the same dataset and then using their predictions as inputs for a meta-model (level-1 model) that makes the final decision14. This approach allows the meta-model to learn how to best combine the strengths of each base model, potentially outperforming any individual model in the ensemble5. Stacking differs from other ensemble methods like bagging and boosting by exploring a diverse space of models for the same problem, rather than focusing on reducing variance or bias4. The technique can be applied to both classification and regression tasks, with implementations available in popular machine learning libraries such as scikit-learn5.

Stacking in AI works by leveraging multiple models in a hierarchical structure to improve overall prediction accuracy. The process typically involves two main levels of models: base models (level-0) and a meta-model (level-1). First, several diverse base models are trained on the original dataset. These models can include different algorithms like decision trees, support vector machines, and neural networks2. The predictions from these base models are then used as input features for the meta-model, which learns to optimally combine their outputs12. This meta-model is trained on a separate validation set to avoid overfitting. During inference, new data is fed through the base models, and their predictions are combined by the meta-model to produce the final output3. This approach allows the system to capture complex patterns and relationships in the data that individual models might miss, potentially leading to improved performance and generalization.

The architecture of a stacking model in machine learning consists of multiple layers that work together to produce a final prediction. Here are the key components of a stacking model:

• Dataset: The initial dataset is split into training and test sets to evaluate the model's performance.3

• Level 0 Models: These are the base prediction models, often referred to as base learners or level-0 models.13

• Level 0 Predictions: The outputs generated by the base models using the training data.3

• Level 1 Model: Also known as the meta-model or meta-learner, this model combines the predictions of the base models.13

• Level 1 Prediction: The final output of the stacked model, which is produced by the meta-model using the predictions from the base models.3

This layered approach allows the stacking model to leverage the strengths of multiple algorithms, potentially leading to improved accuracy and performance compared to individual models.14

Stacking in AI offers several advantages but also comes with some drawbacks. Here's a concise overview of the key pros and cons:

Stacking can significantly boost performance by leveraging the strengths of multiple models, but it requires careful implementation and may not always be the most efficient solution for every problem.12345