A Data Mining Based Fraud Detection Model for Water Consumption Billing System in MOG by Eyad Humaid

Author:Eyad Humaid
Language: eng
Format: azw3
Publisher: Eyad Hashem S.Humaid
Published: 2017-09-02T07:00:00+00:00

2.8 Performance Evaluation For Predictive Modeling

After model building, knowing the power of model prediction on a new instance, is very important issue. Once a predictive model is developed using the historical data, one would be curious as to how the model will perform on the data that it has not seen during the model building process. One might even try multiple model types for the same prediction problem, and then, would like to know which model is the one to use for the real-world decision making situation, simply by comparing them on their prediction performance (e.g., accuracy). How to measure the performance of a predictor? What are the commonly used performance metrics? What is accuracy? How accurately estimate the performance measures?. These questions are answered in this section. First, the most commonly used performance metrics will be described, then a wide range of estimation methodologies are explained and compared to each other. "Performance Metrics for Predictive Modeling In classification problems, the primary source of performance measurements is a coincidence matrix (classification matrix or a contingency table)" [7]. Figure 2.7 shows a coincidence matrix for a two-class classification problem. The equations of the most commonly used metrics that can be calculated from the coincidence matrix is also given in Fig 2.7.

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