# Interpretability ![Source: Interpretable Machine Learning by Christoph Molnar](/files/-LvO4gn82n6KzUH8b4Dm) Interpretability, often used interchangeably with *explainability*, is the degree to which a model's predictions can be explained in straightforward human terms. Deep neural networks are typically "opaque" due to their inherent complexity and can be difficult to decipher. By contrast, many classical machine learning algorithms are interpretable (e.g. linear regression, logistic regression, decision trees) though this is not always the case — SVM and XGBoost are notably difficult to interpret. ### The Importance of **Model Interpretability** The importance of interpretability is relative. For Netflix, if a prediction goes awry and a poor recommendation is made, aside from a monetary loss, the consequence is minimal. In this case, the "risk" incurred by deploying predictive models is easily outweighed by the benefit. But what about when machine learning is used to diagnose patients or determine credit worthiness? In these cases, explainability is not only important, it may be a regulatory concern -- especially in heavily regulated industries such as banking, medicine, and insurance. There are several open source projects focused on this topic such as **DeepLIFT** and **LIME**. --- # 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/interpretability.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.