Review:
Gradient Boosting Machines
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Gradient boosting machines are a type of machine learning algorithm that builds models in a stage-wise fashion by combining the predictions of multiple simpler models.
Key Features
- Ensemble learning
- Boosting technique
- Sequential training of weak learners
Pros
- High predictive accuracy
- Handles complex relationships in data well
- Works well with large datasets
Cons
- Can be computationally expensive
- May overfit on small datasets