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’s the uncomfortable truth:
🚨 We still evaluate ML models using LogReg-style thinking—expecting every variable to follow a clear, linear trend that aligns with business sense.
This expectation is not just unrealistic—it’s fundamentally flawed.
🏛️ The Comfort of Logistic Regression
LogReg is transparent and interpretable. We expect:
✅ Higher income → Lower risk
✅ More late payments → Higher risk
Because LogReg assumes a linear relationship, we can interpret each variable directly. If a trend contradicts expectations, we investigate data issues, multicollinearity, or transformations.
🌳 Machine Learning: A Different Beast
ML models—whether it’s LightGBM, XGBoost, or deep learning—operate on an entirely different principle. They don’t rely on simple linear relationships; instead, they identify complex, non-obvious patterns.
📌 Example: ML Model Predicting Loan Defaults
• High income ≠ Lower risk if the borrower also has multiple recent credit inquiries.
• Zero late payments ≠ Low risk if they have a history of short-term, high-interest loans.
🚨 Yet, we still insist on seeing traditional, easy-to-interpret variable trends—as if forcing ML to behave like LogReg will somehow make it more trustworthy.
But let’s be honest:
❌ Just because an ML model’s trends align with business sense doesn’t mean it makes good predictions.
❌ Just because a variable’s direction “looks right” doesn’t mean it’s actually driving the model.
🔍 The Solution? Stop Forcing ML to be LogReg
Instead of bending ML models into old-school interpretability methods, we should use the right tools:
🔹 SHAP (Shapley Additive Explanations) – Instead of guessing how a feature behaves, SHAP directly quantifies its impact on predictions.
🔹 Partial Dependence Plots (PDP) – Helps visualize how a variable influences risk over different value ranges.
🔹 LIME (Local Interpretable Model-Agnostic Explanations) – Explains individual predictions rather than overall model structure.
🔹 Counterfactuals – Answers “What if?” questions to make models actionable for decision-makers.
💡 It’s time for us to accept that ML isn’t LogReg—and stop treating it like it is.
Do you agree, or do you think traditional validation approaches should still apply? Let’s discuss. 🚀
