In this section, you will learn why ensemble methods are usually superior to individual machine learning models. Furthermore, you will learn about the technique of bagging. Both are essential features of random forests.
In machine learning, an ensemble method is a machine learning model that aggregates the predictions of individual models. Since ensemble methods combine the results of multiple models, they are less prone to error, and therefore tend to perform better.
Imagine your goal is to determine whether a house will sell within the first month of being on the market. You run several machine learning algorithms and find that logistic regression gives 80% accuracy, decision trees 75% accuracy, and k-nearest neighbors 77% accuracy.
One option is to use logistic regression, the most accurate model, as your final model. A more compelling option is to combine the predictions of each individual model.
For classifiers, the standard option...