--- title: Medium | daily.dev description: A build-in-public log documenting the completion of ML documentation for a social media engagement prediction system (BufferIQ). Covers an ensemble... canonical: https://daily.dev/posts/medium-megr8nsrt twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Medium | daily.dev og:description: A build-in-public log documenting the completion of ML documentation for a social media engagement prediction system (BufferIQ). Covers an ensemble... og:url: https://daily.dev/posts/medium-megr8nsrt og:image: https://api.daily.dev/og/posts/megr8Nsrt.png og:image:alt: Medium 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. # Medium **[Medium](https://daily.dev/sources/medium_js)** · 10 min read · 0 upvotes · 0 comments ## Summary A build-in-public log documenting the completion of ML documentation for a social media engagement prediction system (BufferIQ). Covers an ensemble GradientBoostingRegressor with a 41-feature vector, per-platform evaluation metrics (R²=0.86 LinkedIn, 0.81 Twitter, 0.74 Bluesky), chronological train/val/test split rationale, hyperparameter decisions with justification for choosing gradient boosting over neural networks on small per-user datasets, voice profile cosine-distance deviation scoring, and a SHAP worked example tracing a 43.1 prediction to its top contributing features. Both model cards follow Google format with explicit ethical considerations including engagement-optimization bias risks. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Day 30 Part 4: ML Documentation Live, Day 30 Closed](https://daily.dev/posts/day-30-part-4-ml-documentation-live-day-30-closed-wdyhrtnuh) · Medium · 0 upvotes · 0 comments - [How LinkedIn Feed Uses LLMs to Serve 1.3 Billion Users](https://daily.dev/posts/how-linkedin-feed-uses-llms-to-serve-1-3-billion-users-wymxiezwf) · ByteByteGo · 20 upvotes · 1 comments - [How Malt’s ML Team Engineered a 14% Conversion Lift](https://daily.dev/posts/how-malt-s-ml-team-engineered-a-14-conversion-lift-hrqysr1qm) · Medium · 0 upvotes · 0 comments - [Budget Allocation at Scale: Building an ML Optimization System for Paid Marketing](https://daily.dev/posts/budget-allocation-at-scale-building-an-ml-optimization-system-for-paid-marketing-avblznziq) · Atlassian · 0 upvotes · 0 comments - [Building a hybrid demand forecasting service: classical ML for the number, an LLM for the why](https://daily.dev/posts/building-a-hybrid-demand-forecasting-service-classical-ml-for-the-number-an-llm-for-the-why-bfbys6aak) · Medium · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning) [View this post on daily.dev](https://daily.dev/posts/medium-megr8nsrt) ```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":"Medium","url":"https://daily.dev/posts/medium-megr8nsrt","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/medium-megr8nsrt"},"datePublished":"2026-07-20T20:04:52.861Z","dateModified":"2026-07-20T20:05:57.604Z","description":"A build-in-public log documenting the completion of ML documentation for a social media engagement prediction system (BufferIQ). Covers an ensemble...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/d6ab0a9a223ee01bc82d647f54feb2d2?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/d6ab0a9a223ee01bc82d647f54feb2d2?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/medium-megr8nsrt","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Medium"}]} ```