# Metrics in Machine Learning In the context of machine learning, a metric is any number that we care about. An *objective* is a specific type of metric that a machine learning system attempts to optimize. ## Technical Metrics [Accuracy](/wiki/accuracy-and-loss.md) is the most common (and easy to understand) metric but tracking only accuracy will paint an incomplete picture of how your model is performing. There are several other well-established metrics that provide deeper insight into model performance. Metrics are often specific to the type of machine learning problem or model. Important and widely adopted metrics include: Accuracy, [Loss](/wiki/accuracy-and-loss.md#loss), [Confusion Matrix](/wiki/confusion-matrix.md), [AUC (Area Under ROC curve)](/wiki/auc-area-under-the-roc-curve.md), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R Square. ## Correlating to Business Metrics Machine learning metrics are often directly correlated to business metric. One example would be assigning a dollar value to *false positives* in a classification model. Here's a great [example](https://medium.com/airbnb-engineering/fighting-financial-fraud-with-targeted-friction-82d950d8900e) of how AirBnB measures the performance of their fraud prediction algorithm in dollars. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/metrics-in-machine-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.