Metrics for Evaluation of Machine Learning Algorithms

After processing the data and training the model the next step is to check how effective the model is. Different performance metrics are used to evaluate different Machine Learning Algorithms:

Accuracy

Accuracy is a good measure when the target variable classes in the data are nearly balanced. Accuracy is a relevant measure for binary classifier. For a binary classifier that classifies instances into positive (1) and negative (0) instances, any single prediction can fall in to one of four terms in the below.