Decision trees – the unreasonable power of nested decision rules
6 hours ago
- #Decision Trees
- #Machine Learning
- #Entropy
- Decision Trees classify data by partitioning the feature space into regions using sequential rules.
- Entropy measures information and quantifies the purity of data samples, with pure samples having zero entropy.
- Decision Trees use entropy in information gain and the ID3 algorithm to determine splitting rules.
- Decision Trees are simple, interpretable, and fast but suffer from instability and sensitivity to small data changes.
- Overfitting in Decision Trees can be mitigated by pruning techniques like limiting tree depth or leaf size.
- High variance in Decision Trees can be addressed by using ensembles like random forests.
- Decision Trees can be overly complex if not constrained, leading to poor generalization.
- Future topics include Decision Trees for regression and other tree-specific hyperparameters.