Review:
Gradient Boosting Machines
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Gradient Boosting Machines (GBM) are a popular machine learning technique used for both regression and classification problems. They build decision trees sequentially in a gradient-boosted model to improve accuracy.
Key Features
- Ensemble method
- Sequential building of decision trees
- Boosting algorithm
Pros
- High predictive accuracy
- Handles large datasets well
- Reduces overfitting compared to traditional decision trees
Cons
- Prone to overfitting if hyperparameters are not tuned properly
- Can be computationally expensive