A decision tree is a simple procedure that identifies variables that provide optimal separation of classes (node purity) by splitting the data on their values.

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Decision trees can only be used for classification problems.

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The Gini index is referred to as a measure of node purity. This is used to evaluate variable importance in classification problems. A small value indicates that a node contains predominantly observations from a single class; therefore, a predictor that results in a relatively large Gini decrease indicates that when the data are split on the respective predictor, there is considerable class separation.

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One of the primary advantages of using decision trees is that it is very easy to explain how the model arrived at its prediction for a given observation.

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Random forests provide a lift in predictive power over decision trees. Random forests involve building many decision trees (a forest of trees) on different random subsets of both observations and predictors. The performance is then averaged over all the trees to protect against overfitting.

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In a classification problem, the ability to explain how the model arrived at its prediction is of less importance relative to a regression problem.

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The business objective drives this decision in both classification and regression problems.