Tell us why is “naive” bayes naive?
Answer / Hem Chandra Pant
Naive Bayes is called 'naive' because it makes an assumption that the features (or attributes) of a problem are conditionally independent given the class variable. This simplifying assumption allows for efficient computation, but may not hold true in real-world scenarios. However, despite this 'naiveness', Naive Bayes performs well in many practical applications due to its ability to capture complex probabilistic relationships among features.
| Is This Answer Correct ? | 0 Yes | 0 No |
How do I become a machine learning scientist?
Do I need to learn python for machine learning?
What’s your favorite algorithm, and can you explain it to me in less than a minute?
What is the difference between bayesian and frequentist?
What are the components of relational evaluation techniques?
Define A HashTable in Machine Learning?
What is dimensionality reduction?
What are the differences between machine learning and artificial intelligence?
Which os is good for machine learning?
What is an Incremental Learning algorithm in ensemble?
Can r be used for machine learning?
What are the different methods of Sequential Supervised Learning?
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)