|
|
| Line 1: |
Line 1: |
| − | [http://www.youtube.com/results?search_query=Gradient+Boosting+Algorithms Youtube search...] | + | [http://www.youtube.com/results?search_query=Regularization+Overfitting Youtube search...] |
| − | | |
| − | * [[Gradient Descent Optimization & Challenges]]
| |
| − | * [[Objective vs. Cost vs. Loss vs. Error Function]]
| |
| − | | |
| − | Gradient Boosting Algorithm uses multiple weak algorithms to create a more powerful accurate algorithm. Instead of using a single estimator, having multiple will create a more stable and robust algorithm. The specialty of Gradient Boosting Algorithms is their higher accuracy. There are several Gradient Boosting Algorithms. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium]
| |
| − | | |
| − | * [http://xgboost.readthedocs.io/en/latest/ XGBoost] — uses liner and tree algorithms
| |
| − | * [http://lightgbm.readthedocs.io/en/latest/ LightGBM] — uses only tree-based algorithms; has incredible high performance as well.
| |
| | | | |
| | <youtube>u73PU6Qwl1I</youtube> | | <youtube>u73PU6Qwl1I</youtube> |
