04: K-Nearest neighbor

Question 1

KNN - K-Nearest Neighbors

Answer 1

ML algorithm that is used to classify data points based on their proximity to other data points

Question 2

K-Neighbors classification

Answer 2

Commonly used:
* Pattern recognition
* Image classification
* Recommendation system

Performance depends on:
* The choice of K
* The distance metric

Question 3

K-Neighbors Regression

Answer 3

• Suitable for continuous numerical predictions
• Works well with small to medium-sized datasets

Question 4

Streanghts of KNN?

Answer 4

• Simple and easy to understand
• Effective for small datasets
• No training phase
• Works well with minimal Hyperparameter tuning
• Flexible distance metrics
• Good baseline model

Question 5

Weaknesses of KNN?

Answer 5

• Computationally expensive for large datasets
• Not suitable for sparse data
• Slow prediction speed
• Choice of k is crucial
• Affected by Noisy and Irrelevent features

Question 6

Key Parameters in KNN

Answer 6

1. Number of Neighbors (k)
2. Distance Metric

Question 7

Applications of KNN

Answer 7

• Disease diagnosis and Patient risk assessment
• Object identification
• Facial recognition
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