Decision Trees and Random Forests for Classification and Regression pt.1

Want to use something more interpertable, something that trains faster and performs pretty much just as well as the old Logistic Regression or even Neural Networks? You should consider Decision Trees for classification and regression. Decision Trees and their extension Random Forests are robust and easy-to-interpret machine learning algorithms for Classification and Regression tasks. Decision Trees and Decision Tree Learning together comprise a simple and fast way of learning a function that maps data x to outputs y, where x can be a mix of categorical and numeric variables and y can be categorical for classification, or numeric for regression. Methods such as SVMs, Logistic Regression and Deep Neural Nets pretty much do the same thing.