--- title: How to Train a Scoring Model in the Age of Artificial... description: A structured methodology for building credit scoring models using logistic regression, covering candidate model training, statistical validation, performance... canonical: https://daily.dev/posts/how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence-ns6kgmudc twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How to Train a Scoring Model in the Age of Artificial Intelligence | daily.dev og:description: A structured methodology for building credit scoring models using logistic regression, covering candidate model training, statistical validation, performance... og:url: https://daily.dev/posts/how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence-ns6kgmudc og:image: https://api.daily.dev/og/posts/Ns6kgMuDC.png og:image:alt: How to Train a Scoring Model in the Age of Artificial Intelligence 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. # How to Train a Scoring Model in the Age of Artificial Intelligence **[Towards Data Science](https://daily.dev/sources/tds)** · 17 min read · 0 upvotes · 0 comments ## Summary A structured methodology for building credit scoring models using logistic regression, covering candidate model training, statistical validation, performance evaluation, and final model selection. The process evaluates models across four criteria families: statistical validity (likelihood ratio tests, VIF, coefficient direction), predictive performance (AUC, Gini, F1, PR-AUC), temporal stability (train/test/out-of-time comparison with penalized Gini), and interpretability. OpenAI Codex is used as a coding assistant to automate repetitive tasks like variable combination loops and metric computation. The final selected model uses 4 variables, achieves ~60% Gini and ~49% PR-AUC, and demonstrates no overfitting across time periods. The article argues logistic regression remains the preferred production model in credit risk despite more complex ML alternatives, due to its interpretability, stability, and regulatory alignment. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How to Build a Credit Scoring Grid From a Logistic Regression Model](https://daily.dev/posts/how-to-build-a-credit-scoring-grid-from-a-logistic-regression-model-lftmdwx1l) · Towards Data Science · 0 upvotes · 0 comments - [How to Select Variables Robustly in a Scoring Model](https://daily.dev/posts/how-to-select-variables-robustly-in-a-scoring-model-bxxyo63aa) · Towards Data Science · 0 upvotes · 0 comments - [Building Robust Credit Scoring Models with Python](https://daily.dev/posts/building-robust-credit-scoring-models-with-python-avvk552gx) · Towards Data Science · 1 upvotes · 0 comments - [How to Study the Monotonicity and Stability of Variables in a Scoring Model using Python](https://daily.dev/posts/how-to-study-the-monotonicity-and-stability-of-variables-in-a-scoring-model-using-python-qazuozcwo) · Towards Data Science · 0 upvotes · 0 comments - [Building Robust Credit Scoring Models \(Part 3\)](https://daily.dev/posts/building-robust-credit-scoring-models-part-3--kwlzqkx9i) · Towards Data Science · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#logistic-regression](https://daily.dev/tags/logistic-regression) [View this post on daily.dev](https://daily.dev/posts/how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence-ns6kgmudc) ```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":"How to Train a Scoring Model in the Age of Artificial Intelligence","url":"https://daily.dev/posts/how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence-ns6kgmudc","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence-ns6kgmudc"},"datePublished":"2026-06-10T17:08:15.343Z","dateModified":"2026-06-10T17:08:43.575Z","description":"A structured methodology for building credit scoring models using logistic regression, covering candidate model training, statistical validation, performance...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/d73f6610312067b32cd2a24f6e8a5a4c?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/d73f6610312067b32cd2a24f6e8a5a4c?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","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":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence-ns6kgmudc","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,python,logistic-regression","timeRequired":"PT17M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"How to Train a Scoring Model in the Age of Artificial Intelligence"}]} ```