Random Forest
Random Forest is an ensemble learning method primarily used for classification and regression tasks. It was introduced by Leo Breiman in 2001 and is based on constructing a multitude of decision trees during training. Each decision tree operates independently on a subset of the data, and the results are aggregated to make a final prediction. For classification, the majority vote across trees determines the output, while in regression, the average prediction is used. This method capitalizes on the principle of bagging to reduce overfitting and enhance generalization.
https://en.wikipedia.org/wiki/Random_forest
A unique feature of Random Forest is its ability to select a random subset of features at each split, which introduces diversity among the decision trees. This characteristic minimizes the correlation between individual trees, improving overall model robustness and accuracy. Random Forest also provides metrics like feature importance, allowing users to understand which features contribute most to predictions. This interpretability makes Random Forest widely used in domains such as bioinformatics, finance, and marketing.
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
Despite its strengths, Random Forest has limitations, such as computational inefficiency with very large datasets or high-dimensional data due to the extensive training of multiple trees. However, modern implementations like Scikit-learn, H2O.ai, and Spark MLlib provide optimized versions of Random Forest that can handle larger datasets efficiently. Its ability to handle missing values and its resilience to noisy data continue to make it a popular choice in machine learning applications.
https://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd