Tree-based Methods
Decision trees are a simple model for decision. Yet combined with other methods, decision trees can be quite powerful.
3 Random Forest
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Category: { Machine Learning }
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References:
- Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
- Biau, G. (2012). Analysis of a Random Forests Model. J. Mach. Learn. Res., 13, 1063–1095.
Summary: random forest in machine learning
Pages: 3
2 Decision Tree
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Category: { Machine Learning }
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Summary: In this article, we will explain how decision trees work and build a tree by hand.
The code used in this article can be found in this repo. Definition of the problem We will decide whether one should go to work today. In this demo project, we consider the following features.
feature possible values health 0: feeling bad, 1: feeling good weather 0: bad weather, 1: good weather holiday 1: holiday, 0: not holiday For more compact notations, we use the abstract notation $\{0,1\}^3$ to describe a set of three features each with 0 and 1 as possible values. In general, the notation $\{0,1\}^d$ indicates $d$ binary features.
Pages: 3
1 Tree-based Learning
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Category: { Machine Learning }
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Summary: Tree-based learning
Pages: 3