Study of Parametric Performance Evaluation of Machine Learning and Statistical Classifiers
Yugal Kumar, Girija Shankar Sahoo · International Journal of Information Technology and Computer Science · 2013
Most of the researchers/ scientists are facing data exp losion problem presently.Large amount of data is availab le in the world i.e. data fro m science, industry, business, survey and many other areas.The main task is how to prune the data and extract valuable information fro m these data which can be used for decision making.The answer of this question is data mining.Data Mining is popular topic among researchers.There is lot of work that cannot be explo red in the field of data mining till now.A large number of data mining tools/software's are available which are used for mining the valuable info rmation fro m the datasets and draw new conclusion based on the mined informat ion .These tools used different type of classifiers to classify the data.Many researchers have used different type of tools with different classifiers to obtained desired results.In this paper three classifiers i.e.Bayes, Neural Network and Tree are used with two datasets to obtain desired results.The perfo rmance of these classifiers is analyzed with the help of Mean Absolute Error, Root Mean-Squared Error, Time Taken, Co rrectly Classified Instance, Incorrectly Classified instance and Kappa Statistic parameter.