--- title: Introduction to Quantile regression | daily.dev description: Quantile regression extends traditional regression by predicting multiple percentiles of the outcome distribution rather than just the mean. Instead of a... canonical: https://daily.dev/posts/introduction-to-quantile-regression-x4ulwuliz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Introduction to Quantile regression | daily.dev og:description: Quantile regression extends traditional regression by predicting multiple percentiles of the outcome distribution rather than just the mean. Instead of a... og:url: https://daily.dev/posts/introduction-to-quantile-regression-x4ulwuliz og:image: https://api.daily.dev/og/posts/x4uLWuliZ.png og:image:alt: Introduction to Quantile regression og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Introduction to Quantile regression **[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 5 min read · 0 upvotes · 0 comments ## Summary Quantile regression extends traditional regression by predicting multiple percentiles of the outcome distribution rather than just the mean. Instead of a single point estimate, it provides quantile-level predictions (e.g., 25th, 50th, 75th percentiles) by training separate models with asymmetric loss functions. The technique uses quantile loss (pinball loss) with a weight parameter that assigns different penalties to over- and under-predictions, pulling the regression line toward desired percentiles. Implementation involves optimizing model weights using scipy's minimize function for different quantile values, and the approach works particularly well with tree-based models like LightGBM. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How are QQ Plots Created?](https://daily.dev/posts/how-are-qq-plots-created--lgtirc3ni) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments - [A Visual Explanation of Linear Regression](https://daily.dev/posts/a-visual-explanation-of-linear-regression-9bt0oisrn) · Towards Data Science · 0 upvotes · 0 comments - [Linear Probability Model: Definition, Examples & Limitations](https://daily.dev/posts/linear-probability-model-definition-examples-limitations-jl7tauiez) · DigitalOcean Community · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#data-science](https://daily.dev/tags/data-science), [#data-analysis](https://daily.dev/tags/data-analysis) [View this post on daily.dev](https://daily.dev/posts/introduction-to-quantile-regression-x4ulwuliz) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Introduction to Quantile regression","url":"https://daily.dev/posts/introduction-to-quantile-regression-x4ulwuliz","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/introduction-to-quantile-regression-x4ulwuliz"},"datePublished":"2026-01-26T21:44:15.026Z","dateModified":"2026-01-26T21:44:35.165Z","description":"Quantile regression extends traditional regression by predicting multiple percentiles of the outcome distribution rather than just the mean. 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