Stacking in Machine Learning

Stacking is a ensemble learning technique where the final model known as the “stacked model" combines the predictions from multiple base models. The goal is to create a stronger model by using different models and combining them.

Architecture of Stacking

Stacking architecture is like a team of models working together in two layers to improve prediction accuracy. Each layer has a specific job and the process is designed to make the final result more accurate than any single model alone. It has two parts:

1. Base Models (Level-0)

These are the first models that directly learn from the original training data. You can think of them as the “helpers” that try to make predictions in their own way.

• Base models can be Decision Tree, Logistic Regression, Random Forest, etc.
• Each model is trained separately using the same training data.

2. Meta-Model (Level-1)

This is the final model that learns from the output of the base models instead of the raw data. Its job is to combine the base models predictions in a smart way to make the final prediction.

• A simple Linear Regression or Logistic Regression can act as a meta-model.
• It looks at the outputs of the base models and finds patterns in how they make mistakes or agree.

Steps to Implement Stacking

• Start with training data: We begin with the usual training data that contains both input features and the target output.
• Train base models: The base models are trained on this training data. Each model tries to make predictions based on what it learns.
• Generate predictions: After training the base models make predictions on new data called validation data or out-of-fold data. These predictions are collected.
• Train meta-model: The meta-model is trained using the predictions from the base models as new features. The target output stays the same and the meta-model learns how to combine the base model predictions.
• Final prediction: When testing the base models make predictions on new, unseen data. These predictions are passed to the meta-model which then gives the final prediction.

With stacking we can improve our models performance and its accuracy.

Advantages of Stacking

Here are some of the key advantages of stacking:

• Better Performance: Stacking often results in higher accuracy by combining predictions from multiple models making the final output more reliable.
• Combines Different Models: It allow to use various types of models like decision trees, logistic regression, SVM made from each model’s unique strengths.
• Reduces Overfitting: When implemented with proper cross-validation it can reduce the risk of overfitting by balancing out the weaknesses of individual models.
• Learns from Mistakes: The meta-model is trained to recognize where base models go wrong and improves the final prediction by correcting those errors.
• Customizable: We can choose any combination of base and meta-models depending on our dataset and problem type making it highly flexible.

Limitations of Stacking

Stacking also have some limitations as well:

• Complex to Implement: Compared to simple models or even bagging/boosting, stacking requires more steps and careful setup.
• Slow Training Time: Since you're training multiple models plus a meta-model it can be slow and computationally expensive.
• Hard to Interpret: With multiple layers of models it becomes difficult to explain how the final prediction was made.
• Risk of Overfitting: If the meta-model is too complex or if there's data leakage it can overfit the training data.
• Needs More Data: It performs better when you have enough data, especially for training both base and meta-models effectively

Some of the most popular ensemble techniques include Bagging and Boosting.

• Bagging trains multiple similar models and averages their predictions to reduce mistakes.
• Boosting creates a series of models that correct the errors made by previous ones.

Read More:

• Ensemble Learning
• Types of Ensemble Learning
• Bagging vs Boosting in Machine Learning