What are the two paradigms of ensemble methods?
- 1 Answers
- 281 Views
- I also Faced
- E-Mail Answers
Answer / Mohit Kumar Prasad
The two main paradigms of ensemble methods in machine learning are: (1) Bagging, which creates multiple bootstrap samples from the original dataset and trains a separate model on each sample; (2) Boosting, which trains models iteratively by weighting instances based on their past errors and combining predictions.
| Is This Answer Correct ? | 0 Yes | 0 No |
What Is The Difference Between Machine Learning And Data Mining?
Mention key business metrics that help ML?
Describe the relationship between machine learning and artificial intelligence?
What do you think of our current data process?
What is the difference between covariance and correlation?
What is the difference between heuristic for rule learning and heuristics for decision trees?
How does gaussian naive bayes work?
What is your favorite algorithm and also explain the algorithm in briefly in a minute?
Tell me how is knn different from k-means clustering?
Which language is better for machine learning?
What is connectionist AI?
Explain what are some methods of reducing dimensionality?
-
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)
