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In machine learning, Stacking" or Stacked Ensembling refers to a technique used to improve the predictive performance of a model by combining the predictions of multiple base models. Stacking is a type of ensemble learning method that takes the outputs of several base models and uses another model, often called a "meta-learner" or "stacking model," to make a final prediction based on these outputs.

Here's how stacking typically works:

1. Base Models: You start by training several different machine learning models on your training data. These models can be of different types (e.g., decision trees, support vector machines, neural networks) or variations of the same type with different hyperparameters.

2. Predictions from Base Models: After training, you use these base models to make predictions on the same validation or test data for which you want to make predictions.

3. Meta-Learner: You then build another model, often called a meta-learner or stacking model. This model takes the predictions made by the base models as its input features. Instead of using the raw features of your data, the meta-learner uses the base model predictions as the input features to learn how to combine and weight these predictions optimally.

4. Training the Meta-Learner: You train the meta-learner on the same training data, using the true target values as labels. The meta-learner learns to weigh the predictions of the base models to minimize some loss function, effectively learning the best way to combine the base models' outputs.

5. Making Final Predictions: Once the meta-learner is trained, you can use it to make predictions on new, unseen data. It takes the predictions of the base models as input and produces the final prediction.

Stacking is a powerful technique because it can capture patterns and relationships among the base model predictions that individual models might miss. It helps improve the overall predictive performance by reducing bias and variance and can lead to better generalization.

However, stacking can be computationally expensive and requires careful model selection, hyperparameter tuning, and validation to ensure it provides benefits over using a single model or other ensemble methods like bagging or boosting. Additionally, it's important to use a diverse set of base models to maximize the potential benefits of stacking.

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How stacking works: Use three different base models and a meta-learner (usually a simple model like Logistic Regression) to perform stacking on a synthetic dataset. Code:
         
       Output:    
         
         
         

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