IGNOU MCS 221 2026 2027 - Compare decision tree, Naïve Bayes, k-nearest nei

Question

Compare decision tree, Naïve Bayes, k-nearest neighbour and support vector machine classifiers. Recommend the most appropriate algorithm for credit-risk prediction with justification.

30 Jul 2026

Answer :

Word Count : 683

Classification is one of the most important tasks in data mining, where a model is trained using historical data to predict the class label of new instances. Decision Tree, Naïve Bayes, k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) are among the most widely used classification algorithms. Each algorithm differs in its learning approach, computational complexity, interpretability, and suitability for different types of datasets. A Decision Tree is a supervised learning algorithm that classifies data by recursively splitting it into subsets based on the most informative attributes. Internal nodes represent decision conditions, branches represent outcomes of those conditions, and leaf nodes represent class labels. Algorithms such as ID3, C4.5, and CART are commonly used for constructing decision trees. Decision trees are easy to understand and interpret, can handle both numerical and categorical data, and require little data preprocessing. However, they are prone to overfitting, especially when the tree grows very deep. Techniques such as pruning and ensemble methods can reduce this limitation. Naïve Bayes is a probabilistic classifier based on Bayes' Theorem. It assumes that all predictor variables are conditionally independent given the class label, an assumption that is rarely true

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