Review:
Gaussian Process Regression
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Gaussian process regression is a non-parametric approach for regression analysis that provides a probabilistic prediction of the target variable based on training data.
Key Features
- Flexibility in modeling complex relationships
- Provides uncertainty estimates
- Can handle small datasets efficiently
Pros
- Flexibility in capturing non-linear patterns
- Ability to quantify uncertainty in predictions
- Effective for small datasets with noisy or sparse data points
Cons
- Computationally expensive for large datasets
- Limited scalability to high-dimensional data or massive datasets