How is a "random forest" defined in machine learning?

A "random forest" is defined as an ensemble method that employs multiple decision trees to make predictions or classifications. This approach combines the outputs of various decision trees, which are constructed based on random subsets of the data and features, to enhance the overall predictive performance and accuracy.

The primary advantage of using a random forest lies in its ability to reduce overfitting, which is a common issue in single decision trees. By aggregating the predictions from multiple trees, the random forest averages their results, which leads to more robust and reliable predictions. This method takes advantage of the diversity among the different trees, allowing it to capture complex patterns in the data while minimizing errors that could arise from the peculiarities of individual trees.

Furthermore, random forests can also handle large datasets with higher dimensionality and are less sensitive to noise. Their ability to provide feature importance insights, highlighting which variables contribute most to predictions, adds to their practical utility in various data science applications.

This understanding of ensemble learning using multiple decision trees is fundamental for grasping how random forests operate and why they are a powerful tool in machine learning.