Comparative Study on Performance Evaluation of Eager versus Lazy Learning Methods

The major revenue in banking sector is generated long term deposits from customers. Many marketing strategies are implemented to target potential customers by examining their impacted characteristics for decision making. Therefore, machine learning as a scientific computing has drawn many interest in finding best potential customers especially in predicting whether a long term deposit is subscribed or not. In this research, lazy and eager learning of K-Nearest Neighbours (KNN) and Random Forest (RF) is compared. The computation procedure of the prediction makes a sharp distinction between them and accordingly, RF is proven to be more superior than KNN in the term of Accuracy as much as 96%, Precision 93% and F1 score 0.97. Therefore, the ultimate performance of RF relies on the ability to handle non-linearities and its resistance to overfitting makes RF a suitable choice for many predictive applications. Keywords— Classification; Easy learning; Lazy Learning, Term Deposit