Here's some decision tree learners that were pretty cool at the time. Decision tree learning is the construction of a decision tree from class-labeled training

Here's some decision tree learners that were pretty cool at the time. Decision tree learning is the construction of a decision tree from class-labeled training tuples. A decision tree is a flow-chart-like structure, where each internal (non-leaf) node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. There are many specific decision-tree algorithms. Notable ones include: ID3 (Iterative Dichotomiser 3) C4.5 (successor of ID3) CART (Classification And Regression Tree) CHAID (CHi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.[11] MARS: extends decision trees to handle numerical data better. Conditional Inference Trees. Statistics-based approach that uses non-parametric tests as splitting criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased predictor selection and does not require pruning.[12][13] ID3 and CART were invented independently at around the same time (between 1970 and 1980)[citation needed], yet follow a similar approach for learning a decision tree from training tuples.