Machine Learning for Digit Recognition: kNN Model Error Analysis [Video]

This video presents a comprehensive analysis of machine learning models applied to two datasets:

Prostate Cancer Dataset – Model Comparison:

We evaluate and compare six models: kNN (1NN, 7NN, 9NN) and Decision Trees (cp = 0, 0.05, and 0.1).
Using ROC Curves, we identify the best-performing model based on its ability to balance accuracy and generalization.
MNIST Handwritten Digit Recognition – Error Analysis:

Focusing on digits ‘1’ and ‘7’, we analyze the errors made by the kNN model.
Examples of False Positives (when a ‘7’ is misclassified as ‘1’) and False Negatives (when a ‘1’ is misclassified as ‘7’) are shown and explained.
We discuss why these errors occur and highlight the limitations of kNN for visual data.
This video demonstrates:

How to compare machine learning models effectively.
The importance of evaluating model errors to understand performance limitations.
The role of model complexity in balancing overfitting and generalization.
If you are a student, researcher, or professional interested in model evaluation, ROC analysis, and digit classification challenges, this video will give you valuable insights.