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Extremely Randomized Tree Ensemble Classifier

The Extremely Randomized Trees (Extra-Trees) Classifier is an ensemble model that builds multiple randomized decision trees and averages their predictions. This approach aims to improve accuracy and reduce overfitting.

Advantages:​

  • Robust to overfitting
  • Highly parallelizable
  • Provides feature importance
  • Quick to train
  • Versatile

Disadvantages:​

  • Low interpretability
  • High memory usage
  • Predictive variability
  • Less accurate than some boosting methods
  • Requires hyperparameter tuning