What is Accuracy in Machine Learning?

Accuracy in machine learning is a measure of how well a model is able to predict the correct output for a given input. It is one of the most commonly used metrics in machine learning and is used to evaluate the performance of a model. Accuracy is calculated by comparing the predicted output of a model to the actual output. The higher the accuracy, the better the model is performing. Accuracy can be measured on a variety of datasets, including classification, regression, and clustering tasks. It is important to note that accuracy is not always the best metric to measure a model’s performance, as other metrics such as precision and recall may be more appropriate for certain tasks.