ABSTRACT
On a specific computer or system, performance modelling is a powerful equation for estimating and assessing project management success (typically in terms of execution time). Models of performance can reveal important information regarding efficiency limitations. Machine learning is becoming a greater part of our lives as it is used to overcome academic and commercial challenges. To provide significant value to a company, machine learning algorithms must be able to make accurate predictions. This chapter discusses various methods of measuring the performance of machine learning algorithms, such as confusion matrix, accuracy, precision, etc., and the methods of evaluation of performance of regression models like root mean square error, R square, etc. In this chapter, we will also go over the methods for determining how effectively a machine learning algorithm generalises to new, previously unknown data. We will also learn how to use Python to construct standard model assessment metrics for regression and classification issues.
