Comparing Different Machine Learning Techniques for Classifying Multi Label Data

In recent years, multi-label classifications have become common. Multi label classification is a in which collection of labels associated with single instance, may be variation the label. The problem huge data each instance different kind further can identified more than one class. various machine learning strategies for classifying are discussed this paper. Many researches been carried out that specify grouping multiple labels. Here we will compare techniques involve two approaches: adapted algorithm approach and method transformation. using naive multinomial bayes logistic regression. We use certain evaluation metrics to predict differences as well. Better methods