Understanding Overfitting In Predictive Analytics

🚀 Deep Learning Playlist by Nitish Singh: Lectures 21–30 In Lectures 21–30, I moved deeper into improving, stabilizing, and optimizing neural networks. 🔍 Key Learnings 21: Improving Neural Network Performance Explored the core parameters that influence model performance: • Hidden layers & neurons • Learning rate • Batch size • Activation functions • Epochs Also learned common challenges like insufficient data, vanishing gradients, overfitting, and slow training — and how optimization methods, transfer learning, and regularization help. 22: Early Stopping Understood overfitting and how early stopping prevents it by monitoring validation loss. Learned how to tune “patience” and other parameters, and how tracking training vs validation curves shows when the model begins to memorize rather than learn. 23: Normalization & Standardization Learned why scaling inputs (like Age vs Salary) is essential for stable learning. • Normalization → [0,1] range • Standardization → mean=0, std=1 Applied these techniques and saw faster convergence and improved model stability. 24–25: Dropout (Theory + Practice) Dropout = randomly turning off neurons during training to avoid overfitting. Saw its effect on: • Regression • Classification Learned how dropout rate p changes model behavior (low p → overfitting, high p → underfitting) and how CNNs/RNNs need different ratios. 26–27: Regularization (L1/L2) Understood why overfitting happens and how L1 & L2 regularization reduce model complexity by penalizing large weights. Implemented L1/L2 and compared performance with vs without regularization. Also explored data augmentation and simplifying architecture. 28: Activation Functions — Dying ReLU Studied the dying ReLU problem, where neurons permanently output zero and stop learning. Causes include: • High learning rate • Negative bias Learned fixes: • Lower LR • Add positive bias • Use Leaky ReLU / PReLU to keep gradients flowing. 29–30: Weight Initialization (What NOT to do → What to do) Covered why bad initialization causes vanishing/exploding gradients. ❌ Zero initialization ❌ Same-value initialization ❌ Very small/very large random values Then learned correct methods: ✔ Xavier Initialization (for sigmoid/tanh) ✔ He Initialization (for ReLU/Leaky ReLU) Understanding initialization made it clear why deep networks need proper variance to train efficiently. 💡 Core Takeaways 🔹 Proper scaling, regularization, and initialization are just as important as architecture. 🔹 Overfitting can be controlled through dropout, early stopping, and L2 regularization. 🔹 Weight initialization + activation function pairing dramatically impacts training stability. 🔹 A well-tuned neural network learns faster, generalizes better, and avoids vanishing/exploding gradients. ✨ Reflection These lectures strengthened my understanding of why neural networks behave the way they do — and how small design choices can make a big difference in performance.

🧵 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

You're in a Senior MLE interview at Google DeepMind. The interviewer sets a trap: "Our new foundation model is overfitting severely on the training set. Should we cut the hidden dimension size from 4096 to 1024 to limit its capacity?" 90% of candidates walk right into it. The candidates say: "Yes. Overfitting means the model has too much capacity, it's memorizing noise instead of learning patterns. We should reduce the number of parameters (neurons/layers) to force generalization." It feels logical as a textbook answer. It's also the wrong architectural move. The reality is that they aren't optimizing for parameter efficiency, they are optimizing for the loss landscape. When you starve a network by reducing its size, you aren't just preventing overfitting, you are creating a harder optimization problem. Smaller networks have complex, non-convex loss landscapes filled with nasty local minima. They struggle to converge at all. ----- 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The Senior Engineer knows the real rule of thumb: 𝘕𝘦𝘷𝘦𝘳 𝘶𝘴𝘦 𝘮𝘰𝘥𝘦𝘭 𝘴𝘪𝘻𝘦 𝘢𝘴 𝘢 𝘳𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘦𝘳. Instead, you lean into 𝐓𝐡𝐞 𝐎𝐯𝐞𝐫-𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐏𝐚𝐫𝐚𝐝𝐨𝐱. You keep the massive architecture (or even make it bigger). You want the high capacity to ensure the model has the power to learn complex boundaries easily. Then, you aggressively constrain the weights using actual regularizers. - Keep the 4096 hidden units. - Crank up the regularization (𝘓𝘢𝘮𝘣𝘥𝘢/𝘞𝘦𝘪𝘨𝘩𝘵 𝘋𝘦𝘤𝘢𝘺) or 𝘋𝘳𝘰𝘱𝘰𝘶𝘵. We trade the risk of "underfitting due to small size" for the manageable challenge of "tuning λ." 𝐓𝐡𝐞 𝐀𝐧𝐬𝐰𝐞𝐫 𝐓𝐡𝐚𝐭 𝐆𝐞𝐭𝐬 𝐘𝐨𝐮 𝐇𝐢𝐫𝐞𝐝: "I wouldn't touch the architecture size. It's easier to regularize a large model than to train a small one. I’d keep the capacity high to ensure learnability, then increase the regularization term λ to penalize the weights until generalization improves." #MachineLearning #DeepLearning #ComputerVision #DataScience #Engineering #InterviewTips #AI

What if you could see the exact point where your model starts overfitting as you tune hyperparameters? Hyperparameter tuning requires finding the sweet spot between underfitting (model too simple) and overfitting (model memorizes training data). You could write the loop, run cross-validation for each value, collect scores, and format the plot yourself. But that's boilerplate you'll repeat across projects. Yellowbrick is a machine learning visualization library built for exactly this. Its ValidationCurve shows you what's working, what's not, and what to fix next without the boilerplate or inconsistent formatting. How to read the plot in this example: • Training score (blue) stays high as max_depth increases • Validation score (green) drops after depth 4 • The growing gap means the model memorizes training data but fails on new data Action: Pick max_depth around 3-4 where validation score peaks before the gap widens. 🚀 Full article: https://bit.ly/4qn5Qeq ☕️ Run this code: https://bit.ly/48RanQp #Python #MachineLearning #DataScience #Yellowbrick

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

What is the essence of "overfitting" data? Here is a new way to look at it: -->> It all comes down to how many observations are used to form a prediction. In ML models, unfortunately, this view is lost because models throw away the data and only keep thousands of complex parameter values. But in this paper we show how, beneath the surface of a high-complexity ML model, its learned logic can be as simple as putting 100% weight on a single data point for each prediction and ignoring all the others. Model complexity hides these basic facts. Data-level transparency -- showing how past data points extrapolate to each prediction -- leads to a dramatically simpler understanding of what a model is really doing. Most importantly, we show how an alternative approach called relevance-based prediction (RBP) starts from the premise of selecting the right observations to inform each prediction. By retaining the data and using it directly, we can avoid overfitting and provide intuitive transparency at the same time. Our full paper coauthored with Megan Czasonis and Mark Kritzman is available at the link below. Please share your thoughts! #machinelearning #prediction #datascience #relevance #transparency

💥 𝗪𝗵𝘆 𝗔𝗜 𝗙𝗮𝗶𝗹𝘀 (sometimes). One of the biggest challenges in building reliable AI is managing the "bias–variance tradeoff." It shows up every time we try to build machine learning models that perform well in the real world, not just in development. For example, here’s a simplified version of what the tradeoff looks like when predicting the risk of diabetes complications. 𝗕𝗶𝗮𝘀: 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝘁𝗼𝗼 𝘀𝗶𝗺𝗽𝗹𝗲 (𝘂𝗻𝗱𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴) A high-bias model might only use a few features (such as age, A1C, and BMI) to predict the risk of diabetes. With such limited inputs, the model misses meaningful relationships in the data, such as comorbidities, socioeconomic factors, and time-series trends. Because it cannot capture these patterns, it performs poorly on both the patient training data and new patients. 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲: 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗼𝗼 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 (𝗼𝘃𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴) At the other extreme, imagine a model with hundreds of features and deep interactions: numerous lab values over time, medication timelines, encounter histories, and embeddings from clinical notes. A model this complex may start learning noise in the training dataset (patterns that aren’t clinically meaningful or generalizable). It performs extremely well in development but struggles when deployed on a new patient population. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳: 𝗙𝗶𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝘄𝗲𝗲𝘁 𝗦𝗽𝗼𝘁 Sticking with our example, as we increase the complexity of the diabetes model, bias decreases because the model can represent richer patterns. But variance increases because the model becomes sensitive to small fluctuations in the training data. The optimal model sits in the middle: complex enough to detect meaningful signals associated with diabetes complications, but not so complex that it breaks down in real-world settings. 𝗛𝗼𝘄 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗧𝗵𝗶𝘀 Teams rely on approaches like: 👉 regularization methods such as lasso and ridge regression (which discourages overly complex models), 👉 ensemble methods such as random forests and boosting (to reduce variance or balance error), 👉 cross-validation to "tune hyperparameters" (to find the optimal point of complexity where the error is minimized across patient subsets), 👉 careful feature engineering and selection, 👉 and expanding or diversifying training data. These techniques keep the model in the zone where it generalizes well. 𝗪𝗵𝘆 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗮𝗿𝗲 AI/ML models that underfit will miss patients who are truly at risk for all types of acute and chronic conditions. Models that overfit may trigger unnecessary alarms for low-risk patients. Neither outcome supports good clinical care. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Understanding the bias–variance tradeoff is essential to building reliable AI tools that clinicians and patients can trust!

*** Overfitted and Underfitted Models *** ~ In mathematical modeling, overfitting produces an analysis that corresponds too closely or exactly to a particular data set and may fail to fit additional data or predict future observations reliably. ~ An overfitted model is a mathematical model that contains more parameters than can be justified by the data. The essence of overfitting is to have unknowingly extracted some of the residual variation (i.e., the noise) as if that variation represented the underlying model structure. ~ Underfitting occurs when a mathematical model cannot adequately capture the underlying structure of the data. ~ An underfitted model is a model where some parameters or terms that would appear in a correctly specified model are missing. ~ Underfitting would occur, for example, when fitting a linear model to nonlinear data. Such a model will tend to have poor predictive performance. ~ The possibility of over-fitting exists because the criterion used for selecting the model is not the same as the criterion used to judge the suitability of a model. ~ For example, a model might be chosen by maximizing its performance on some set of training data, and yet its suitability might be determined by its ability to perform well on unseen data; overfitting occurs when a model begins to "memorize" training data rather than "learning" to generalize from a trend. ~ Overfitting is directly related to the approximation error of the selected function class and the optimization error of the optimization procedure. A function class that is too large relative to the dataset size will likely overfit. ~ Even when the fitted model does not have an excessive number of parameters, it is expected that the fitted relationship will appear to perform less well on a new dataset than on the dataset used for fitting (a phenomenon sometimes known as shrinkage). ~ In particular, the coefficient of determination will shrink relative to the original data. Several techniques (e.g., model comparison and cross-validation) are available to lessen the chance or amount of overfitting. ~ The basis of some methods is to either (1) explicitly penalize overly complex models or (2) test the model's ability to generalize by evaluating its performance on a set of data not used for training, which is assumed to approximate the typical unseen data that a model will encounter. --- B. Noted

AI Made Simple Series: Overfitting 🤔What is Overfitting? In AI, overfitting happens when a model learns the training data too well—even the noise and random details—making it perform poorly on new, unseen data. 📉 🤖 How is it used in AI? AI models aim to learn patterns that generalize to new data. Overfitting is a challenge we work to avoid. Techniques like regularization, early stopping, or using more data help keep models balanced between learning and generalizing. 🌟 Real-life use cases: 1️⃣ Credit Scoring: Overfitting might cause a model to deny a good customer due to overly specific patterns in training data. 2️⃣ Medical Diagnosis: A model might misdiagnose new patients if it overfits to specific cases from the training dataset. Overfitting is a common hurdle in building reliable AI models. What strategies do you use to handle overfitting in your projects? Or do you have questions about it? Share your thoughts below! 👇 #ArtificialIntelligence #Overfitting #MachineLearning #DeepLearning #DataScience #AIExplained #TechEducation #AIApplications #LearningAI #ModelOptimization #AIForBeginners

Here's a tricky ML interview question that often confuses candidates: 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧: You have a dataset with 100 features, and you trained a model that performs well on training data but poorly on test data. You suspect overfitting. If you remove some of the features, could the test accuracy increase? Why or why not? 𝐀𝐧𝐬𝐰𝐞𝐫: Yes, removing some features can increase test accuracy. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐲: 📍 𝐂𝐮𝐫𝐬𝐞 𝐨𝐟 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲: When the number of features is very high relative to the number of data points, the model may memorize training data instead of generalizing well. Reducing features can help avoid overfitting. 📍 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐨𝐫 𝐑𝐞𝐝𝐮𝐧𝐝𝐚𝐧𝐭 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: Some features may not contribute meaningful information to the target variable or may introduce noise. Removing them reduces unnecessary complexity, leading to better generalization. 📍 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐬 𝐌𝐨𝐝𝐞𝐥 𝐒𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲: Reducing dimensions helps the model learn more robust patterns instead of fitting noise, leading to better test performance. 📍 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Fewer features reduce the risk of overfitting because the model has fewer parameters to optimize, making it less prone to capturing random variations in training data. 𝐇𝐨𝐰 𝐭𝐨 𝐑𝐞𝐦𝐨𝐯𝐞 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐒𝐦𝐚𝐫𝐭𝐥𝐲? 📌 Feature Importance (e.g., using Random Forest, XGBoost feature importance, or SHAP values). 📌 Lasso Regression (L1 regularization) to shrink less important feature coefficients to zero. 📌 PCA (Principal Component Analysis) to reduce dimensions while preserving variance. #machinelearning #interview #datascience #ai Follow Sneha Vijaykumar for more... 😊