Candidate Elimination Algorithm

 Introduction The Candidate Elimination Algorithm (CEA) is a supervised technique used for concept learning from data. It involves formulating a proper concept function using a dataset of objects labelled as positive or negative. The algorithm works by considering a hypothesis space that contains candidate hypotheses expressed through a specific representation. These hypotheses are then partially ordered to find the version space, which includes all hypotheses consistent with both positive and negative objects in the data. The CEA relies on an inductive bias, which is a set of assumptions that guide the search towards specific types of hypotheses. This bias allows for deductive reasoning, ensuring that the version spaces output correct labels. The main goal of the CEA is to converge towards a specific hypothesis that accurately classifies objects based on the training data. Algorithm Step1: Load Data set Step2: Initialize General Hypothesis and Specific Hypothesis. Step3: ...