Decision Trees are a supervised machine learning modeling method. The following tutorial will step through a review of modeling in general, fitting and over-fitting, supervised learning styles, classification, training and testing, labels, decision tree examples, confusion matrices and cost matrices, issues with measuring accuracy, other measures such as precision and recall, details and math of decision trees (GINI, Entropy, Information Gain, etc.), Hunt’s algorithm, and node splitting options.