Evaluating the Performance of Machine Learning Models Using Metrics

Machine learning has gained widespread popularity in recent years. It allows us to draw inferences about new situations using previous data, and there are a wide range of algorithms available for this purpose. Popular machine learning algorithms used in the modern day world include linear regression, naïve Bayes, random forests, logistic regression and also some others like k-means clustering and decision trees. When making predictions with machine learning, we often try out multiple algorithms to determine which one produces the most accurate results on the data. Keywords: Evaluation Metrics, Confusion Matrix, Prediction Score, Classification