A Comparison Between Classification Algorithms on Different Datasets Methodologies using Rapidminer

Abstract: Data Mining techniques are helpful in finding out patterns between data attributes and results in probalistic prediction of the label attribute.The paper discusses different classification techniques on small and large datasets.The two datasets are example datasets used from repository sites depending upon the number of instances.These datasets were applied in different classifier like Random Forest, Naive Bayes and Decision Tree to identify the best classifier for small dataset and large dataset. This paper gives the study and analysis of various methodologies used for prediction Based on the study, Na�ve Bayes is most suitable for small datasets and Decision Tree is suitable for large datasets based on the evaluation done in this paper using various methodologies driven by RapidMiner tool while equating precision, recall and accuracy.

Keywords: Naive Bayes, Random Forest, Decision Tree,RapidMiner tool.