Implementing the AdaBoost Algorithm with Code
📌 The process involves using Python libraries like Scikit-learn (ML) and Matplotlib for visualization and demonstrating the AdaBoost algorithm step-by-step.
📊 The initial dataset contains 10 rows with 3 features (X₁, X₂, X₃) and one binary output column, exhibiting a slightly non-linear pattern.
⚙️ The first step after data setup is assigning an initial equal weight of 0.1 to every row (since there are 10 rows) before training the first weak classifier.

Training and Weight Updating in AdaBoost
📉 After training the first decision stump, the error rate is calculated; for example, the first model showed errors on rows 2, 6, and 8.
💡 The model weight ($\alpha$) is calculated using the formula related to the error rate $\epsilon$ as $\alpha = \frac{1}{2} \ln \left(\frac{1-\epsilon}{\epsilon}\right)$, which helps determine the influence of that weak learner.
⚖️ The weights of misclassified samples are increased by multiplying them by $e^{\alpha}$, while correctly classified samples' weights are multiplied by $e^{-\alpha}$, followed by normalization so the total sum of weights equals 1.0.

Understanding Model Weight (Alpha) Significance
📈 The multiplication factor $e^{-\alpha}$ (or $e^{\alpha}$) is crucial; the video explains its relationship to the model's "overfitting" tendency, where a higher $\alpha$ (low error) means the new weight updates will be larger.
🚫 A check is included for the scenario where the error rate $\epsilon$ is $0$ (perfect classification), in which case a small epsilon ($\epsilon + 10^{-10}$) must be added to the denominator to prevent division by zero.

Resampling and Iterative Training
🔄 A new dataset is generated using weighted random sampling (bootstrapping) based on the updated weights, causing samples that were previously misclassified (having higher weights) to appear more frequently.
🔁 This resampling creates the training set for the second weak learner, ensuring that the subsequent models focus more heavily on the previously difficult-to-classify data points.
🎯 After training three successive weak learners (stumps), their final predictions are combined by multiplying each prediction by its corresponding model weight ($\alpha$) and summing the results to determine the final, robust prediction (using the sign of the sum).

Key Points & Insights
➡️ Initial weight assignment is uniform across all samples (1/N rows) before the first weak learner is trained.
➡️ The model weight $\alpha$ effectively controls how much the current weak learner will influence the final ensemble decision.
➡️ After weight updates, it is mandatory to re-normalize all sample weights so their sum equals 1.0 before proceeding to the next iteration of resampling.
➡️ Prediction in AdaBoost is a weighted majority vote, where the sign of the sum of weighted predictions determines the final class output.

📸 Video summarized with SummaryTube.com on Nov 28, 2025, 06:24 UTC