{"id":2602,"date":"2025-02-20T18:49:54","date_gmt":"2025-02-20T11:49:54","guid":{"rendered":"https:\/\/mintea.blog\/?p=2602"},"modified":"2025-02-21T15:55:34","modified_gmt":"2025-02-21T08:55:34","slug":"2602","status":"publish","type":"post","link":"https:\/\/mintea.blog\/?p=2602","title":{"rendered":"Are We Forcing Machine Learning to Fit the Logistic Regression Mindset?"},"content":{"rendered":"

Are We Forcing Machine Learning to Fit the Logistic Regression Mindset?<\/span><\/h3>\n

Link: https:\/\/www.linkedin.com\/embed\/feed\/update\/urn:li:share:7297637290665820160<\/a><\/p>\n

In risk model validation, I often see a fundamental misalignment between how we validate traditional models like Logistic Regression (LogReg) and how we approach Machine Learning (ML) models. And here\u2019s the uncomfortable truth:<\/span><\/p>\n

\ud83d\udea8 We still evaluate ML models using LogReg-style thinking\u2014expecting every variable to follow a clear, linear trend that aligns with business sense.<\/span><\/p>\n

This expectation is not just unrealistic\u2014it\u2019s fundamentally flawed.<\/span><\/p>\n

\ud83c\udfdb\ufe0f The Comfort of Logistic Regression<\/span><\/p>\n

LogReg is transparent and interpretable. We expect:<\/span>
\n\u2705 Higher income \u2192 Lower risk<\/span>
\n\u2705 More late payments \u2192 Higher risk<\/span><\/p>\n

Because LogReg assumes a linear relationship, we can interpret each variable directly. If a trend contradicts expectations, we investigate data issues, multicollinearity, or transformations.<\/span><\/p>\n

\ud83c\udf33 Machine Learning: A Different Beast<\/span><\/p>\n

ML models\u2014whether it\u2019s LightGBM, XGBoost, or deep learning\u2014operate on an entirely different principle. They don\u2019t rely on simple linear relationships; instead, they identify complex, non-obvious patterns.<\/span><\/p>\n

\ud83d\udccc Example: ML Model Predicting Loan Defaults<\/span>
\n\u2022 High income \u2260 Lower risk if the borrower also has multiple recent credit inquiries.<\/span>
\n\u2022 Zero late payments \u2260 Low risk if they have a history of short-term, high-interest loans.<\/span><\/p>\n

\ud83d\udea8 Yet, we still insist on seeing traditional, easy-to-interpret variable trends\u2014as if forcing ML to behave like LogReg will somehow make it more trustworthy.<\/span><\/p>\n

But let\u2019s be honest:<\/span>
\n\u274c Just because an ML model\u2019s trends align with business sense doesn\u2019t mean it makes good predictions.<\/span>
\n\u274c Just because a variable\u2019s direction \u201clooks right\u201d doesn\u2019t mean it\u2019s actually driving the model.<\/span><\/p>\n

\ud83d\udd0d The Solution? Stop Forcing ML to be LogReg<\/span><\/p>\n

Instead of bending ML models into old-school interpretability methods, we should use the right tools:<\/span><\/p>\n

\ud83d\udd39 SHAP (Shapley Additive Explanations) \u2013 Instead of guessing how a feature behaves, SHAP directly quantifies its impact on predictions.<\/span>
\n\ud83d\udd39 Partial Dependence Plots (PDP) \u2013 Helps visualize how a variable influences risk over different value ranges.<\/span>
\n\ud83d\udd39 LIME (Local Interpretable Model-Agnostic Explanations) \u2013 Explains individual predictions rather than overall model structure.<\/span>
\n\ud83d\udd39 Counterfactuals \u2013 Answers \u201cWhat if?\u201d questions to make models actionable for decision-makers.<\/span><\/p>\n

\ud83d\udca1 It\u2019s time for us to accept that ML isn\u2019t LogReg\u2014and stop treating it like it is.<\/span><\/p>\n

Do you agree, or do you think traditional validation approaches should still apply? Let\u2019s discuss. \ud83d\ude80<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Are We Forcing Machine Learning to Fit the Logistic Regression Mindset? Link: https:\/\/www.linkedin.com\/embed\/feed\/update\/urn:li:share:7297637290665820160 In risk model validation, I often see a fundamental misalignment between how we validate traditional models like Logistic Regression (LogReg) and how we approach Machine Learning (ML) models. And here\u2019s the uncomfortable truth: \ud83d\udea8 We still evaluate ML models using LogReg-style thinking\u2014expecting … Continue reading Are We Forcing Machine Learning to Fit the Logistic Regression Mindset?<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[106],"tags":[37,100,105,104,99,52,98,41],"class_list":["post-2602","post","type-post","status-publish","format-standard","hentry","category-posts","tag-banking","tag-linked-discussion","tag-linkedin","tag-linkedin-discussion","tag-logistic-regression","tag-machine-learning","tag-modeling","tag-risk"],"_links":{"self":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts\/2602","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2602"}],"version-history":[{"count":5,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts\/2602\/revisions"}],"predecessor-version":[{"id":2607,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts\/2602\/revisions\/2607"}],"wp:attachment":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2602"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2602"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2602"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}