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Random forests are an ensemble of decision trees that improve prediction accuracy from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Random forests are a powerful technique for predictive modeling that leverages the concept of ensemble learning. In ensemble learning, multiple models are trained on the data and their predictions are combined to make a final prediction. The idea is that by combining the predictions of multiple models, we can achieve better performance than any single model on its own. Decision trees are a popular choice for the base models in ensemble learning because they are simple to understand and can capture complex relationships in the data. Random forests take this idea a step further by combining multiple decision trees into a single model. Each decision tree in the random forest is trained on a slightly different subset of the data, which ...
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