Interested to Buy Any Domain ? << Click Here >> for more details...
What is the meaning of overfitting in machine learning?
- 1 Answers
- 135 Views
- I also Faced
- E-Mail Answers
Answer / Pradeep Kumar Mishra
Overfitting in machine learning refers to a situation where a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts the model's ability to generalize and make accurate predictions on unseen data.
| Is This Answer Correct ? | 0 Yes | 0 No |
What is sequence classification?
Why naive bayes is called naive?
What is the objective of machine learning?
What is the main difference between a Pandas series and a single-column DataFrame in Python?
What are the different methods for Sequential Supervised Learning?
Explain the benefit of naive bayes mcq?
What is the method to avoid overfitting?
What are the two paradigms of ensemble methods?
what is the function of ‘unsupervised learning’?
What is the difference between inductive machine learning and deductive machine learning?
Is naive bayes a supervised or unsupervised method?
Explain the importance of bayes theorem?
-
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)
