Construct a decision tree given an order of testing the features. Determine the prediction accuracy of a decision tree on a test set. Compute the entropy of a probability distribution.

Classification with Decision Tree Induction This algorithm makes Classification Decision for a test sample with the help of tree like structure (Similar to Binary Tree OR k-ary tree) Nodes in the tree are attribute names of the given data Branches in the …

A classification technique (or classifier) is a systematic approach to building classification models from an input data set. The training data consist of pairs of input objects (typically vectors), and desired outputs. The output of the function can be a continuous value (called regression), or can be a categorical value (called classification).

Examples include decision tree classifiers, rule-based classifiers, neural networks, support vector machines, and na¨ıve Bayes classifiers. Each technique employs a learning algorithm to identify a model that best fits the relationship between the attribute set and class label of the input data. The model generated by a learning algorithm

Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model.

Decision tree is a hierarchical data structure that represents data through a di-vide and conquer strategy. In this class we discuss decision trees with categorical labels, but non-parametric classi cation and regression can be performed with decision trees as well. In classi cation, the goal is to learn a decision tree that represents the training

Basic Algorithm for Top-Down Learning of Decision Trees [ID3, C4.5 by Quinlan] node= root of decision tree Main loop: 1. Aßthe “best” decision attribute for the next node. 2.Assign Aas decision attribute for node. 3.For each value of A, create a new descendant of node. 4.Sort training examples to leaf nodes.

Regression algorithms can draw a boundary line between the data. Decision Trees introduces a threshold for each axis individually. But if keep introducing axis-aligned splits (the tree becomes bigger ) and we end up overfitting.

Start from the root of tree. How to learn a decision tree? There could be more than one tree that fits the same data! If Dt contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset.

Algorithm for Decision Tree Induction n Basic algorithm (a greedy algorithm) n Tree is constructed in a top-down (from general to specific) recursive divide-and-conquer manner n At start, all the training examples are at the root n Attributes are categorical (if continuous-valued, discretization in …