Review:
Gradient Boosting Algorithm
overall review score: 4.5
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score is between 0 and 5
Gradient boosting algorithm is a machine learning technique used for regression and classification problems. It builds models in a stage-wise fashion, combining multiple weak learners to create a strong predictive model.
Key Features
- Stage-wise learning
- Combining multiple weak learners
- Model optimization
Pros
- High predictive accuracy
- Handles complex relationships in data
- Robust to overfitting
Cons
- Can be computationally expensive
- Requires careful tuning of hyperparameters