Classification and Regression Trees: The use and significance of Trees in analytics

Abstract

A classification or regression treecan be used to depict adecision tree, which is a prediction model. One of the oldest and most essential methods is the classification and regression tree technique. It's a technique for predicting outcomes based on a set of predictor factors. In data mining, decision trees often create a model used to that predicts the targetofthe values based on the many input variables. Some of thetechniques used in predictive modelling are data mining, machine learning, statistics, and is decision tree learning, also known as induction of decision trees.It is a term used for classification and regression trees describe decision tree algorithms used for that are CART learning tasks.The models are created by recursively dividing the data space and fitting a basic prediction model to each division. Equally a consequence, the dividing may be seen as a decision tree visually. Regression trees are used to model continuous or ordered discrete dependent variables using error prediction, which is measured as the squared difference between actual and predicted values.For dependent variables with an unordered value of the finite number, classification trees are used, The cost of misclassification is used to quantify prediction inaccuracy.