Review:
Gradient Boosting
overall review score: 4.5
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score is between 0 and 5
Gradient boosting is a machine learning technique used for regression and classification problems. It builds models by combining multiple weak learners in a sequential manner.
Key Features
- Ensemble learning
- Boosting technique
- Sequential model building
- Tree-based models
Pros
- High predictive accuracy
- Can handle complex relationships in data
- Effective in handling large datasets
Cons
- Prone to overfitting if not properly tuned
- Computationally expensive compared to other algorithms