Hey there! 😊 In this lesson, we'll explore an exciting machine learning technique called "Stacking." You might wonder why we’re learning about stacking and how it helps us make better predictions. Well, imagine asking several experts for their opinions and combining them to make a decision. By the end of this lesson, you'll know how to implement and use stacking to boost model performance!

Let's dive into stacking. Stacking is an ensemble technique combining multiple models (base models) to produce a final prediction using another model (meta-model). Think of base models as chefs, and the meta-model as a food critic who tastes all the dishes and decides the final rating.

How Does Stacking Work?

1. Training Base Models: Training multiple base models on the same dataset. Each model brings its unique strength to capture different aspects of the data.
2. Generating Meta-Data: Using the base models' predictions, we generate a new dataset (meta-data). This dataset is composed of the predictions of all base models.
3. Training Meta-Model: Training a meta-model on this new meta-data. The meta-model learns how to best combine the predictions of the base models to make the final prediction.

Why stacking?

• Improved Accuracy: Combining different models captures various patterns and reduces errors.
• Reduced Overfitting: Multiple models balance biases and variances.

To get hands-on with stacking, we need data. We'll again use the digit dataset from scikit-learn. This dataset contains images of digits used to predict what digit each image represents.