Machine Learning - Stacking

• Uses weak learners (bareley better than random)

• Computationally efficent and combined to make strong learner

• Ensemble technique

• Hetorogenous models

• Combine using another meta model

• Combine with another ML model

• Split training data into two sets

• Any model can be used to create a stacking model

• For ex you can train 3 models using one set of training data

• Use those models to make predictions on the other training data set

• Then train on another model and make a prediction with that model

Stacking is like assembling a super team of machine learning models! It's a technique to combine multiple models to achieve better predictive performance compared to any single model alone. Here's how it works:

Base Models: First, you have different base models like decision trees, support vector machines, or neural networks. Each one has its own strengths and weaknesses.

Training: Train these models on your training data. They'll learn different aspects of the data due to their diverse structures and algorithms.

Predictions: Now, have each model make predictions on a validation dataset. You now have a new set of predictions from each model.

Stacker Model: Enter the hero, the stacker model! It's trained on these new sets of predictions, learning how to best combine them to make a final prediction.

Final Prediction: When you have new data, you first run it through the base models, get their predictions, and then feed those predictions into the stacker model to get your final prediction.

The magic of stacking lies in its ability to harness the power of diversity among models. It's like each base model brings its own unique perspective to the table, and the stacker model learns how to blend these perspectives to make a more accurate and robust prediction. It’s a fantastic way to squeeze out better performance from your machine learning ensemble!