Simplified Stacking Simulator

Step 1

Train Base Models

These models learn from the original x,y coordinates.

Model A: Decision Tree

Accuracy: --%

Model B: k-Nearest Neighbors

Accuracy: --%

Step 2

Train the "Meta-Model" (The Stacker)

This model learns from the *predictions* of Model A & B, not the original data.

Instead of just picking the best base model or averaging their votes, a stacking ensemble trains a final "Meta-Model" (we're using a Neural Network here).

It looks at what Model A predicts and what Model B predicts, and learns which one to trust in different situations to make the final call.

Final Accuracy Comparison

Model A --%

Model B --%

Stacked Ensemble --%

Stacking Basics

Train Base Models

Model A: Decision Tree

Model B: k-Nearest Neighbors

Train the "Meta-Model" (The Stacker)

Final Accuracy Comparison

Final Stacked Prediction