# 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](https://machine-learning.paperspace.com/wiki/accuracy-and-loss) 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](https://machine-learning.paperspace.com/accuracy-and-loss#loss), [Confusion Matrix](https://machine-learning.paperspace.com/wiki/confusion-matrix), [AUC (Area Under ROC curve)](https://machine-learning.paperspace.com/wiki/auc-area-under-the-roc-curve), 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.