Boosting algorithms
Random forests are a type of bagging algorithm. Bagging combines bootstrapping, selecting individual samples with replacement and aggregation, and combining all models into one ensemble. In practice, a random forest builds individual trees by randomly selecting rows of data, called samples, before combining (aggregating) all trees into one ensemble. Bagging algorithms are as good as the trees that make them up.
A comparable ML algorithm is boosting. The idea behind boosting is to transform a weak learner into a strong learner by modifying the weights for the rows that the learner got wrong. A weak learner may have an error of 49%, hardly better than a coin flip. A strong learner, by contrast, may have an error rate of 1 or 2%. With enough iterations, weak learners can be transformed into very strong learners.
Unlike bagging algorithms, boosting algorithms can improve over time. After the initial model in a booster, called the base learner, all subsequent models...
