# Metrics {% embed url="" %} Metrics - ML Projects {% endembed %} * In most real-world projects, you usually care about a lot of metrics. Because machine learning systems work best when optimizing a single number, you need to pick a formula for combining different metrics of interest. * The first way is to do a simple **average** (or **weighted average**) of these metrics. * The second way is to choose a metric as a **threshold** and evaluate at that threshold value. The thresholding metrics are up to your domain judgment, but you would probably want to choose ones that are least sensitive to model choice and are closest to desirable values. * The third way is to use a more **complex / domain-specific** formula. A solid process to go about this direction is to first start enumerating all the project requirements, then evaluate the current performance of your model, then compare the current performance to the requirements, and finally revisit the metric as your numbers improve. --- # 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://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/metrics.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.