Lecture7: Learning 3
Decision Tree¶
ID3 Algorithm¶
- If Attributes is empty, return the single-node tree, with the label the most common value of Target_attribute in Examples.
The maximum height of the decision tree is the size of Attributes set.
C4.5¶
Random Forest¶
- Different samples
- Different features
Bootstrap Sample¶
- Drawn with replacement
Construction¶
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Assume we have \(n\) examples, \(d\) features
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Bootstrap samples from training data
- Construct each decision tree with randomly sampled \(K \approx \sqrt{d}\) features for each spilt
Deep Learning¶
Deep learning is algorithm that model high-level abstractions in data using architectures consisting of multiple nonlinear transformations.