Further reading
Check out the following resources for more information on the topics covered in this chapter:
- A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning: https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/
- A Kaggler's Guide to Model Stacking in Practice: https://datasciblog.github.io/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/
- Choosing the right estimator: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
- Cross-validation: evaluating estimator performance: https://scikit-learn.org/stable/modules/cross_validation.html
- Decision Trees in Machine Learning: https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052
- Ensemble Learning to Improve Machine Learning Results: https://blog.statsbot.co/ensemble-learning-d1dcd548e936
- Ensemble Methods: https://scikit-learn.org/stable/modules/ensemble.html
- Feature Engineering...
