Machine Learning / 07
Trees & Ensembles
Follow decision trees into bagging, boosting, and ensemble methods.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Trees
- 07.01
Decision Trees
Planned guide: Decision Trees. Focus on intuition, assumptions, and a worked example.
- 07.02
Classification Trees
Planned guide: Classification Trees. Focus on intuition, assumptions, and a worked example.
- 07.03
Regression Trees
Planned guide: Regression Trees. Focus on intuition, assumptions, and a worked example.
- 07.04
Tree vs Linear Models
Planned guide: Tree vs Linear Models. Focus on intuition, assumptions, and a worked example.
- 07.05
Splitting Criteria
Planned guide: Splitting Criteria. Focus on intuition, assumptions, and a worked example.
- 07.06
Entropy
Planned guide: Entropy. Focus on intuition, assumptions, and a worked example.
- 07.07
Information Gain
Planned guide: Information Gain. Focus on intuition, assumptions, and a worked example.
- 07.08
Gini Impurity
Planned guide: Gini Impurity. Focus on intuition, assumptions, and a worked example.
- 07.09
Tree Depth
Planned guide: Tree Depth. Focus on intuition, assumptions, and a worked example.
- 07.10
Pruning
Planned guide: Pruning. Focus on intuition, assumptions, and a worked example.
- 07.11
Tree Overfitting
Planned guide: Tree Overfitting. Focus on intuition, assumptions, and a worked example.
Ensembles
- 07.12
Ensemble Learning
Planned guide: Ensemble Learning. Focus on intuition, assumptions, and a worked example.
- 07.13
Bagging
Planned guide: Bagging. Focus on intuition, assumptions, and a worked example.
- 07.14
Random Forest
Planned guide: Random Forest. Focus on intuition, assumptions, and a worked example.
- 07.15
Voting Classifiers
Planned guide: Voting Classifiers. Focus on intuition, assumptions, and a worked example.
- 07.16
Boosting
Planned guide: Boosting. Focus on intuition, assumptions, and a worked example.
- 07.17
AdaBoost
Planned guide: AdaBoost. Focus on intuition, assumptions, and a worked example.
- 07.18
Gradient Boosting
Planned guide: Gradient Boosting. Focus on intuition, assumptions, and a worked example.
- 07.19
XGBoost
Planned guide: XGBoost. Focus on intuition, assumptions, and a worked example.
- 07.20
LightGBM
Planned guide: LightGBM. Focus on intuition, assumptions, and a worked example.
- 07.21
CatBoost
Planned guide: CatBoost. Focus on intuition, assumptions, and a worked example.
- 07.22
Stacking
Planned guide: Stacking. Focus on intuition, assumptions, and a worked example.
- 07.23
Blending
Planned guide: Blending. Focus on intuition, assumptions, and a worked example.