How is knn different from k-means?
Answer / Shivam Mishra
KNN (k-Nearest Neighbors) and k-Means are two popular machine learning algorithms, but they serve different purposes. KNN is a supervised learning algorithm used for classification and regression tasks where it predicts the output based on the k-nearest training samples to a given point. On the other hand, k-Means is an unsupervised learning algorithm used for clustering tasks where it groups data points into clusters based on their similarities.
| Is This Answer Correct ? | 0 Yes | 0 No |
Do you know what is kernel svm?
What is the difference between covariance and correlation?
How would you evaluate a logistic regression model?
What is symbolic reasoning?
What’s the difference between Type I and Type II error?
Explain the benefit of naive bayes mcq?
Can you explain bias-variance trade-off?
Can r be used for machine learning?
What skills do you need for machine learning?
What is the “curse of dimensionality?
Mention any one of the data visualization tools that you are familiar with?
Explain the decision tree classification?
AI Algorithms (74)
AI Natural Language Processing (96)
AI Knowledge Representation Reasoning (12)
AI Robotics (183)
AI Computer Vision (13)
AI Neural Networks (66)
AI Fuzzy Logic (31)
AI Games (8)
AI Languages (141)
AI Tools (11)
AI Machine Learning (659)
Data Science (671)
Data Mining (120)
AI Deep Learning (111)
Generative AI (153)
AI Frameworks Libraries (197)
AI Ethics Safety (100)
AI Applications (427)
AI General (197)
AI AllOther (6)