B tect unit 3

 

Unit-3 Machine Learning Techniques

 

KCS055 Decision Tree Learning and Instance-Based Learning DECISION TREE LEARNING

 • Decision tree learning is one of the most widely used and practical methods for inductive inference. It is a method for approximating discrete-valued functions that is robust to noisy data and capable of learning disjunctive expressions. 

• Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree. Learned trees can also be re-represented as sets of ifthen rules to improve human readability. These learning methods are among the most popular of inductive inference algorithms and have been successfully applied to a broad range of tasks from learning to diagnose medical cases to learning to assess credit risk of loan applicants. Decision Tree Introduction 

• Classification is a two-step process, learning step and prediction step, in machine learning. In the learning step, the model is developed based on given training data. In the prediction step, the model is used to predict the response for given data. Decision Tree is one of the easiest and popular classification algorithms to understand and interpret. 

• Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, decision tree algorithm can be used for solving regression and classification problems too. Decision Tree 

• A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails) , each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. 

 • The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)). Figure Decision Tree 

• Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. 

 • It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. 

• In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. 

• The decisions or the test are performed on the basis of features of the given dataset. 

• It is called a decision tree because, similar to a tree, it starts with the root node, which expands on further branches and constructs a tree-like structure. 

• Below diagram explains the general structure of a decision tree: 

• Note: A decision tree can contain categorical data (YES/NO) as well as numeric data. Decision Tree Terminologies 

• Root Node: Root node is from where the decision tree starts. It represents the entire dataset, which further gets divided into two or more homogeneous sets. 

• Leaf Node: Leaf nodes are the final output node, and the tree cannot be segregated further after getting a leaf node. 

• Splitting: Splitting is the process of dividing the decision node/root node into sub-nodes according to the given conditions. 

• Branch/Sub Tree: A tree formed by splitting the tree. 

• Pruning: Pruning is the process of removing unwanted branches from the tree. 

• Parent/Child node: The root node of the tree is called the parent node, and other nodes are called the child nodes. DECISION TREE REPRESENTATION 

• Decision trees classify instances by sorting them down the tree from the root to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance, and each branch descending from that node corresponds to one of the possible values for this attribute. An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example. This process is then repeated for the subtree rooted at the new node. Figure 3.1 illustrates a typical learned decision tree 

• This decision tree classifies Saturday mornings according to whether they are suitable for playing tennis. For example, the instance (Outlook = Sunny, Temperature = Hot, Humidity = High, Wind =