Machine learning Ensemble models.
Ensemble learning in machine learning combines multiple weak models to improve prediction accuracy, reduce variance, and prevent overfitting. Key techniques include bagging, which trains models on bootstrapped data subsets and aggregates results, and boosting, which sequentially corrects errors made by prior models. Common algorithms discussed include Random Forest and Decision Trees, with implementation examples using Python's scikit-learn library.
- ▪Ensemble learning improves model performance by combining multiple weak learners.
- ▪Bagging uses bootstrapped data samples and includes algorithms like Random Forest and Bagged Decision Trees.
- ▪Boosting trains models sequentially to correct previous errors, focusing on misclassified data points.
- ▪Random Forest reduces overfitting by using random feature subsets at each node in addition to bagging.
- ▪Examples use the Iris dataset and scikit-learn for implementation and evaluation.
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