A Practical Guide To Working With Testing And Training Data In Ml Projects

This article discusses the importance of training and testing data in machine learning projects. It explains the concept of data training, which involves providing an ML algorithm with labeled examples to help it recognize patterns. It also covers data testing, which involves comparing the model’s output against actual results for each example in the test set. Lastly, it provides best practices for splitting datasets into training and testing sets, such as using a 70/30 or 80/20 ratio and employing stratified sampling to maintain class balance.