The primary purpose of using a decision tree is to create a training model that predicts the class or the value of the target variable through learning simple decision rules deduced from the previous training data. To predict a class label for a record in a classification model using a decision tree, the programmer has to start from the root of the tree.

Tree-based classification uses a series of if-then statements to make predictions from one or more decision trees. The tree-based models can be used for regression to predict numerical values or for classification to predict categorical values. There are multiple algorithms to segregate a node into two or more sub-nodes. This sub-nodes creation increases the homogeneity of the resulting sub-nodes. To put it in simple words, the purity of the node increases with respect to the target variable.

The classification tree represents a structural mapping of binary decisions leading to a decision with regards to the class or interpretation of an object like the pixel. It is commonly referred to as a decision tree, but it is more properly a kind of decision tree leading to categorical decisions.

A classification tree is responsible for labelling, recording and assigning variables to discrete classes. It can provide a confidence measure to validate that the classification is correct. It is built through binary recursive partitioning. Tree-based models empower prediction with high accuracy, stability and ease of interpretation. They map nonlinear relationships, unlike the linear models.

Decision trees in Machine Learning are a non-parametric supervised learning technique to work with classification and regression tasks. Classification trees are the tree models in which the target variables take discrete sets of values.