Random Forests are a powerful and widely used supervised learning algorithm, falling under the umbrella of ensemble methods. Introduced by Leo Breiman in 2001, the core idea is to build a 'forest' of decision trees, each trained on a slightly different subset of the data and features. This 'randomness' comes from two main sources: bootstrap aggregating (bagging) where each tree is trained on a random sample of the training data with replacement, and feature randomness where at each split in a tree, only a random subset of features is considered. By combining the predictions of many decorrelated trees, Random Forests mitigate the high variance and overfitting issues common in individual decision trees, leading to more robust and accurate models. They are highly valued for their interpretability, ability to handle high-dimensional data, and effectiveness across various domains, including finance, healthcare, and cybersecurity, where they are used by data scientists and ML engineers for classification and regression tasks.
Random Forests are a machine learning technique that builds many 'decision trees' and combines their individual predictions to make a final, more accurate decision. This method is very good at avoiding common pitfalls like overfitting, making it reliable for various prediction tasks.
RF, Random Decision Forests, Ensemble of Decision Trees
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