Review:
Semi Supervised Learning
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Semi-supervised learning is a machine learning technique that uses a combination of labeled and unlabeled data to improve predictive models.
Key Features
- Utilizes both labeled and unlabeled data
- Reduces the need for large labeled datasets
- Can achieve high accuracy with limited labeled examples
Pros
- Efficient use of data
- Reduction in labeling costs
- Improved model performance
Cons
- Requires careful selection of unlabeled data
- May be sensitive to the quality of the initial labeled dataset