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Supervised and Unsupervised Machine Learning

Random Forests

SupaeroDataScience/machine-learning

Supervised and Unsupervised Machine Learning

SupaeroDataScience/machine-learning

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Introduction
Introduction
A geometrical approach to ML
A geometrical approach to ML
- SVMs, the bias-variance tradeoff and a bit of kernel theory
- An application of SVMs to multi-label classification
A probabilistic approach to ML
A probabilistic approach to ML
Mimicking the human brain, the connectionist approach
Mimicking the human brain, the connectionist approach
- Neural networks
Commitee-based methods
Commitee-based methods
- Decision trees
- Boosting
- XGBoost
- Bagging
- Random Forests Random Forests
  Table of contents
  - References
- Anomaly Detection
Perspectives
Perspectives
- Explainability in ML

Random Forests🔗

In this class, we will introduce Random Forest as a new boosting machine algorithm using randomness in two ways to incrementally add trees:

By sub-sampling a random training set in the original training set as in bagging methods.
By selecting a random subset of features on which performing tree splits for each choice of split.

The method is then showcased in simple classification tasks.

References🔗

Wikipedia page on Random Forest

Random Forests. Breiman, Leo, Machine learning 45.1 (2001): 5-32.

Understanding random forests: From theory to practice. Louppe, Gilles, PhD thesis.