Goals
- Use SFrames to do some feature engineering.
- Modify the decision trees to incorporate weights.
- Implement Adaboost ensembling.
- Use your implementation of Adaboost to train a boosted decision stump ensemble.
- Evaluate the effect of boosting (adding more decision stumps) on performance of the model.
- Explore the robustness of Adaboost to overfitting.
Packages used
- graphlab
- matplotlib
Used data set
Algorithms used :
- decision trees.
- Adaboost ensembling.
- boosted decision stump ensemble.