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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 introduces diversity into the ensemble. This diversity is important because it helps to reduce overfitting – the tendency for a model to perform well on the training data but poorly on new, unseen data. By combining the predictions of many different decision trees, the random forest is able to generalize well to new data. One of the key benefits of random forests is their ability to handle high-dimensional data with many features. Traditional decision trees can struggle with high-dimensional data because they may become too complex and overfit to the noise in the data. Random forests are more robust to high-dimensional data because they only consider a subset of features at each split in the tree, which helps to prevent overfitting.
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