Evaluation of Healthcare Data in Machine Learning Model Used in Fraud Detection

The concept of designing machine learning model based on binary classification technique should compute on a productive evaluation principle. Designing a machine learning model, get evaluation from different kind of techniques, make enhancement and progress until we accomplish a expected efficiency. Feedback metrics describe the work of a machine learning model. Critical form of feedback metrics is their potential to segregate among machine learning model outcome. Evaluation metric can benefit to describe damage to optimize the machine learning model for a given job while training or testing the model. Before aggregating predicted values, the machine learning model’s accuracy must be determined. Making these kind of decision while training and testing is possible with a clear knowledge of evaluation metrics. In this paper, we will cover the many type of evaluation matrices and it performance.