An Application on Ensemble Learning Using KNIME

Machine learning is the science of computers behaving and learning like humans with the knowledge and data of people’s observations, without being directly programmed. In fact, machine learning is inspired by the learning processes of humans. Among machine learning methods, Bayes’ theorem is an important subject studied in probability theory.Naive Bayes classifier is based on Bayes theorem. The way the algorithm works is that it calculates the probability of each state for an element and classifies it according to its highest probability value. Decision tree is a method based on classification by decomposing the data set according to common features. It consists of “branches”, “leaves” and “roots”, just like real-world trees. In decision trees, the superstructure is the root and the substructure is the leaves. It creates a structure that allows the branches to decide between the root and the leaf. Ensemble learning algorithms improve classification performance by combining many machine learning methods. In this study, decision trees from data mining techniques and naive bayes technique were applied on 215 data set “Academic and Employability Factors Affecting Placement”. As a result of this study, the decision tree accuracy rate is 91,892, the naive bayes accuracy rate is 94,595 and the ensemble learning result is 97,297. Thus, a better result is obtained than the result of both algorithms used. The program is implemented on “Knime” program called as “end-to-end data science”.