How does KNN decide which class to assign to a new test point?

It calculates the Euclidean distance between the test point and all training points, selects the $k$ closest neighbors, and assigns the majority class label.

Home ASSEB AI HubUnit 6: Machine Learning Algorithms (Supervised & Unsupervised Learning)

Class XIASSEB-527Unit 6: Machine Learning Algorithms (Supervised & Unsupervised Learning)

Unit 6: Machine Learning Algorithms (Supervised & Unsupervised Learning)

Master official ASSEB Class XI AI Unit 6: Supervised Learning (Linear Regression, KNN, SVM), Unsupervised Learning (K-Means, PCA), Model Training, and OpenCV Computer Vision.

Theory: 8 Marks

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Practical: 10 Marks

•

13 min read

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Learning Outcomes

Differentiate between Supervised, Unsupervised, and Reinforcement Learning.
Implement Supervised regression (Linear Regression) and classification (KNN, SVM) models.
Understand model training concepts: cross-validation, loss functions, overfitting, regularization, and feature standardization.
Implement Unsupervised algorithms: K-Means clustering and Principal Component Analysis (PCA).
Understand digital image representations in OpenCV: pixel matrices, BGR vs RGB color spaces, and Canny edge detection.

Board Exam Scoring Tips

In 5-mark long answer questions, draw a clear comparison table contrasting Supervised vs. Unsupervised Learning with 3 examples each.
Explain Overfitting vs. Underfitting: Overfitting = high variance (memorizing noise), Underfitting = high bias (too simple).

Key Takeaways

Supervised algorithms require labeled training datasets $(X, y)$; unsupervised algorithms discover latent grouping patterns without external labels.
K-Nearest Neighbors (KNN) classifies points based on majority voting among the $k$ nearest Euclidean neighbors.
K-Means clustering iteratively minimizes within-cluster sum of squares (inertia).
PCA reduces dimensionality by projecting correlated features onto orthogonal principal axes of maximum variance.
OpenCV loads images in BGR format; convert with `cv2.cvtColor(img, cv2.COLOR_BGR2RGB)` before Matplotlib visualization.

Practice Unit MCQ Quiz

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