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Machine Learning
An ensemble learning method that combines multiple decision trees to improve prediction accuracy.
Detailed Explanation
Random Forests is an ensemble learning technique in machine learning that constructs numerous decision trees during training. Each tree provides a prediction, and the final output is determined by combining these, typically through majority voting for classification or averaging for regression. This method reduces overfitting, enhances accuracy, and manages high-dimensional data effectively.
Use Cases
- •Detect fraudulent transactions by combining predictions from multiple decision trees, increasing accuracy and robustness in financial security systems.
Related Terms
Other terms in the Machine Learning category