Interested to Buy Any Domain ? << Click Here >> for more details...
How was bayes’ theorem useful in a machine learning context?
- 1 Answers
- 81 Views
- I also Faced
- E-Mail Answers
Answer / Vaibhav Kumar
Bayes' theorem is a probabilistic method used to update the probability for a hypothesis as more evidence or information becomes available. In machine learning, it is used for classification and prediction problems, particularly in Bayesian networks and Naive Bayes classifiers. These methods allow the model to learn from prior knowledge and adjust its predictions based on new data, improving overall accuracy.
| Is This Answer Correct ? | 0 Yes | 0 No |
Why is bayes theorem important?
Assume that you are working on a data set, explain how would you select important variables?
What is bayes' theorem?
What are convolutional networks? Where can we use them?
What is the “kernel trick” and how is it useful?
What are the functions of unsupervised learning?
What are the important data pre-processing techniques in python machine learning?
what is the function of ‘unsupervised learning’?
What is svm algorithm?
What is bias and variance in a Machine Learning model?
Explain the Algorithm of Decision Trees in Machine Learning?
How would you use fl score?
-
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)
