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Random forests - a free lunch that’s not cursed.

Random forests are a powerful machine learning technique that combines multiple decision trees to enhance predictive accuracy and control overfitting. By aggregating the results of various trees, random forests mitigate the risk of individual trees capturing noise from the training data, leading to more robust and reliable models.

One of the key advantages of random forests is their ability to handle high-dimensional data effectively. They can manage a large number of input variables without the need for variable deletion, making them suitable for complex datasets. Additionally, random forests provide estimates of feature importance, aiding in the identification of the most influential variables in a dataset.

Despite their strengths, it's essential to be mindful of certain limitations when using random forests. They can be computationally intensive, especially with large datasets, and may require careful tuning of parameters to achieve optimal performance. Nonetheless, with appropriate implementation, random forests serve as a versatile and effective tool for both classification and regression tasks in machine learning.

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Random Forests - a free lunch that's not cursed