When evaluating models, choice of evaluation metrics is tied to the specific machine learning task. For example, if you built a classifier to detect spam emails vs. normal emails, then you should consider classification performance metrics, such as average accuracy, log-loss, and AUC. If you are trying to predict a score, such as Google’s daily stock price, then you might want to consider regression metrics like the root mean-squared error (RMSE). If you are ranking items by relevance to a query, such as in a search engine, then you'll want to look into ranking losses such as precision-recall (also popular as a classification metric), or NDCG. These are all examples of task-specific performance metrics.
In a regression task, the model learns to predict numeric scores. An example is predicting the price of a stock on future days given past price history and other information about the company and the market. Another example is personalized recommendations, where the goal is to predict a user’s rating for an item.
Here are a couple ways of measuring regression performance:
Classification is about predicting class labels given input data. In binary classification, there are two possible output classes. In multi-class classification, there are more than two possible classes. An example of binary classification is spam detection, where the input data could be the email text and metadata (sender, sending time), and the output label is either “spam” or “not spam.” Sometimes, people use generic names for the two classes: “positive” and “negative,” or “class 1” and “class 0.”
There are many ways of measuring classification performance: