The Random Forests algorithm uses an ensemble of classification trees (forests), with class prediction based on the majority vote of the ensemble. It can provide an unbiased estimate of the classification error by aggregating cross-validation results using bootstrapped samples, as the forest is built.
The algorithm also provides feature importance measures by calculating the increase of the classification error when it is permuted. A graphical output is generated to summarize its effect on classification performance.
More details about Random Forests can be found here.