Let's illustrate this with help of an example. This process is recursive in nature and is repeated for every subtree rooted at the new nodes. Each node in the tree acts as a test case for some attribute, and each edge descending from that node corresponds to one of the possible answers to the test case. They are used in non-linear decision making with simple linear decision surface.ĭecision trees classify the examples by sorting them down the tree from the root to some leaf node, with the leaf node providing the classification to the example.
Testing Set: Similar to the training set and is used to test the candidate concept and determine its performanceĪ decision tree is a tree-like graph with nodes representing the place where we pick an attribute and ask a question edges represent the answers the to the question and the leaves represent the actual output or class label. Candidate Concept: A concept which we think is the target concept. Sample: A set of inputs paired with a label, which is the correct output (also known as the Training Set). Hypothesis Class: Set of all the possible functions. Target Concept: The function that we are trying to find, i.e., the actual answer. Concept: The function that maps input to output. Attribute: A quantity describing an instance. Instances: Refer to the vector of features or attributes that define the input space. The deeper the tree, the more complex the rules and fitter the model.īefore we dive deep, let's get familiar with some of the terminologies: The decision rules are generally in form of if-then-else statements. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. 
Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. It is one of the most widely used and practical methods for supervised learning. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. Decision Tree Analysis is a general, predictive modelling tool that has applications spanning a number of different areas.
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