An Approach to Decision Tree Induction for Classification
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Abstract
One of the most easiest to understand learning algorithms is Decision tree Induction.For both classification and regression forms of problems, the decision tree may be used. It is possible to use these algorithms to create models that learn information from previous data. To solve problems related to classification, regression, clustering and optimization, algorithms like Decision Trees (ID3, C4.5, C5.0, and CART), Support Vector Machine (SVM), Neural Networks, Linear Regression, K-Nearest Neighbor (KNN) and so on can be used. The accuracy of the forecast is very critical. All the algorithms are explained one by one in this paper and are compared on the basis of efficiency & precision. The various parameters of Decision tree Induction and prediction efficiency metrics are discussed.
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