Understanding Overfitting In Predictive Analytics

I want to show you a clever trick you didn't know before. Imagine you have six months' worth of data. You want to build a model, so you take the first five months to train it. Then, you use the last month to test it. This is a common approach for building machine learning models. Unfortunately, you may find out your model works well with the train data but sucks on the test data. Overfitting is not weird. We've all been there. But often, the worst you can do is try and fix it before understanding why it’s happening. Ask anyone about this, and they will give you their favorite step-by-step guide on regularizing a model. They will jump right in and try to fix overfitting. Don’t do this. There's a different way. A better way. Here is the question I want you to answer before you start racking your brain trying to fix a model: Do your test and training data come from the same distribution? When building a model, we assume the train and test come from the same place. Unfortunately, this is not always the case. Here is where the trick I promised comes in: 1. Put your train and test set together. 2. Get rid of the target column. 3. Create a new binary feature, and set every sample from your train set to 0 and every sample from the test set to 1. This feature will be the new target. Now, train a simple binary classification model on this new dataset. The goal of this model is to predict whether a sample comes from the train or the test split. The intuition behind this idea is simple: If all your data comes from the same distribution, this model won't work. But if the data comes from different distributions, the model will learn to separate it. After you build a model, you can use the ROC-AUC to evaluate it. If the AUC is close to 0.5, your model can't separate the samples. This means your training and test data come from the same distribution. If the AUC is closer to 1.0, your model learned to differentiate the samples. Your training and test data come from different distributions. This technique is called Adversarial Validation. It's a clever, fast way to determine whether two datasets come from the same source. If your splits come from different distributions, you won't get anywhere. You can't out-train bad data. But there's more! You can also use Adversarial Validation to identify where the problem is coming from: 1. Compute the importance of each feature. 2. Remove the most important one from the data. 3. Rebuild the adversarial model. 4. Recompute the ROC-AUC again. You can repeat this process until the ROC-AUC is close to 0.5 and the model can’t differentiate between training and test samples. Adversarial Validation is especially useful in production applications to identify distribution shifts. Low investment with a high return.

🧵 1/ In high-dimensional bio data—transcriptomics, proteomics, metabolomics—you're almost guaranteed to find something “significant.” Even when there’s nothing there. 2/ Why? Because when you test 20,000 genes against a phenotype, some will look like they're associated. Purely by chance. It’s math, not meaning. 3/ Here’s the danger: You can build a compelling story out of noise. And no one will stop you—until it fails to replicate. 4/ As one paper put it: “Even if response and covariates are scientifically independent, some will appear correlated—just by chance.” That’s the trap. https://lnkd.in/ecNzUpJr 5/ High-dimensional data is a story-teller’s dream. And a statistician’s nightmare. So how do we guard against false discoveries? Let’s break it down. 6/ Problem: Spurious correlations Cause: Thousands of features, not enough samples Fix: Multiple testing correction (FDR, Bonferroni) Don’t just take p < 0.05 at face value. Read my blog on understanding multiple tests correction https://lnkd.in/ex3S3V5g 7/ Problem: Overfitting Cause: Model learns noise, not signal Fix: Regularization (LASSO, Ridge, Elastic Net) Penalize complexity. Force the model to be selective. read my blog post on regularization for scRNAseq marker selection https://lnkd.in/ekmM2Pvm 8/ Problem: Poor generalization Cause: The model only works on your dataset Fix: Cross-validation (k-fold, bootstrapping) Train on part of the data, test on the rest. Always. 9/ Want to take it a step further? Replicate in an independent dataset. If it doesn’t hold up in new data, it was probably noise. 10/ Another trick? Feature selection. Reduce dimensionality before modeling. Fewer variables = fewer false leads. 11/ Final strategy? Keep your models simple. Complexity fits noise. Simplicity generalizes. 12/ Here’s your cheat sheet: Problem : Spurious signals Fixes: FDR, Bonferroni, feature selection Problem: Overfitting Fixes:LASSO, Ridge, cross-validation Problem: Poor generalization Fixes: Replication, simpler models 13/ Remember: The more dimensions you have, the easier it is to find a pattern that’s not real. A result doesn’t become truth just because it passes p < 0.05. 14/ Key takeaways: High-dim data creates false signals Multiple corrections aren’t optional Simpler is safer Always validate Replication is king 15/ The story you tell with your data? Make sure it’s grounded in reality, not randomness. Because the most dangerous lie in science... is the one told by your own data. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

Hole #7: The Noise Trap—Threat of "Benign Overfitting" Does your model have more holes than Swiss cheese? One of the most overlooked challenges in machine learning is lack of robustness due to benign overfitting. Our models often look great in development—where the train and test sets come from the same distribution—but run into trouble in production when the input noise or data distribution changes. The result? A rapid performance drop that no one saw coming. This figure illustrates the problem: Perturbed Model Performance (Top-Left): Notice how the AUC drops significantly under noise perturbations. Small changes in inputs can cause large swings in performance—classic fragility. Cluster Residual (Top-Right): Clusters 0 and 8 stand out as the worst in terms of robustness, indicating these segments of the data are especially sensitive to noise. Feature Importance (Bottom-Left): We see which features drive the fragility. “Score,” “Utilization,” and “DTI” are among the top factors contributing to the model’s noise sensitivity. Density Comparison (Bottom-Right): This plot highlights the problem are from Cluster 8. A shift to mid score threatens model robustness. Key Takeaways: Benign Overfitting can mask true risk when train and test data share the same distribution. Production Noise often differs from development, triggering unexpected performance declines. Identifying Fragile Clusters (like clusters 0 and 8 here) is crucial to pinpoint where the model needs improvement. Understanding Feature Drivers of robustness problems (e.g., “Score,” “Utilization,” “Income”) helps us prioritize feature engineering and model tuning. Robustness testing—especially under varying noise conditions—is essential to ensure your model doesn’t crumble when faced with real-world data. By diagnosing where and why a model is overly sensitive, you can shore up these “holes” and build a more stable foundation for long-term success. For more insights on how to test and improve your model’s robustness, check out: https://lnkd.in/eQduNcnr