The Impact of Class Imbalance on Predictive Models

Dealing with imbalanced data is one of the key challenges in machine learning, and how we handle it can make or break the success of our models. When class distributions are skewed, models tend to favor the majority class, leaving critical insights from minority classes unnoticed. To ensure our models are fair, accurate, and robust, we need to employ specialized techniques: ➼ Resampling Techniques: Modify the dataset to balance class distribution, either by oversampling the minority class or undersampling the majority. ➼ Data Augmentation: Create additional data points by tweaking existing ones, enriching the dataset for better training. ➼ SMOTE: Generate synthetic examples for the minority class, leading to a more diverse and balanced dataset. ➼ Ensemble Techniques: Combine multiple models to enhance performance, particularly in imbalanced scenarios. ➼ One-Class Classification: Train a model on a single class and use it to identify new, relevant data points. ➼ Cost-Sensitive Learning: Adjust the cost of misclassification to ensure that errors in minority classes are given the attention they deserve. ➼ Evaluation Metrics: Go beyond accuracy with metrics like precision, recall, and F1 score to better assess model performance on imbalanced data. Handling imbalanced data effectively isn’t just a technical necessity; it’s a step towards more equitable and insightful AI. By leveraging these techniques, we can ensure our models are not only technically sound but also ethically robust. #MachineLearning #DataScience #AI #ImbalancedData #DataEthics #Techniques

🔍 The Hardest Data Science Problem I’ve Solved – And What It Taught Me 🚀 Imagine this: You have a dataset with millions of records, but it’s full of missing values, anomalies, and noise. You need to build a predictive model, but standard techniques fail miserably. What do you do? This was exactly the challenge I faced in a recent supply chain prediction for a complex system. Here’s how I tackled it: 🔥 The Challenges 1️⃣ 50%+ missing data in critical variables – Traditional imputation methods (mean, KNN, regression) led to extreme bias. 2️⃣ Concept drift – The data distribution changed over time, making past models ineffective. 3️⃣ High cardinality categorical variables – One feature had over 100K unique categories, making encoding a nightmare. 4️⃣ Severe class imbalance – The target variable was skewed, with a minority class making up less than 1% of the dataset. The Breakthrough Approach ✔ Adaptive Data Imputation – Instead of using simple imputation, I trained a separate deep learning autoencoder to reconstruct missing values based on hidden patterns in the data. This approach recovered lost information better than traditional methods. ✔ Time-Sensitive Feature Engineering – I used dynamic rolling-window aggregation instead of static feature extraction to capture evolving trends. This significantly improved model stability in a changing environment. ✔ Hierarchical Embedding for High Cardinality Data – Instead of one-hot encoding, I trained an embedding layer within a neural network to map high-cardinality categories into dense vectors, capturing relationships that were impossible with traditional encoding methods. ✔ Customized Sampling Strategy – To fix the class imbalance, I created a stratified augmentation pipeline where the rare class was synthetically generated using SMOTE + adversarial sampling, but in a way that respected real-world constraints. ✔ Meta-Learning for Model Selection – Instead of testing models manually, I built an AutoML pipeline that used Bayesian optimization to select the best algorithm based on a dynamic loss function penalizing false positives more than false negatives. The Impact 1. Model accuracy improved by 30%, reducing false negatives by 50%. 2. Reduced inference time by 60%, making it deployable at scale. 3. Business adoption skyrocketed—stakeholders finally trusted the insights because of interpretable feature engineering. Lesson Learned? The hardest problems in data science aren’t solved with off-the-shelf techniques. They require critical thinking, deep experimentation, and creativity. What’s the toughest data science challenge you’ve faced? Let’s discuss!

Is 92% accuracy at detecting heart attacks using wearable ECGs A.I. good? Sounds like an A-minus right? Unfortunately, no. Probably it’s an F. Here’s why. It’s called “class imbalance”and no, it’s not when a bunch of schoolchildren are on a see saw. Class imbalance in heart attack detection datasets can lead to misleadingly high accuracy metrics while masking critical shortcomings in model performance. See, when the majority of training examples represent non-events (e.g., 90% normal cases), models may achieve apparent “high accuracy” by simply predicting the majority class, while failing to detect true cardiac events. For example, a model achieving 95% accuracy in a dataset with 95% negative cases could theoretically misclassify **all** the positive cases — yet still appear effective. A broken clock tells the correct time twice a day, but AI can fool even skilled readers all the time because of metrics like “accuracy.” There’s something called an F1 Score you should ask for if you’re evaluating these models. If it’s not shown in the paper, be suspicious. An F1 Score is a special type of average that gives more weight to smaller values related to precision and recall — or true positives and false negatives. But that’s missing the point, which is ECGs have a few problems with wearables, one of which is you need a loop from, typically, two different point on be body where the electrodes can sense the voltage across the heart. And for heart attacks, it’s not like there’s one single pattern —- time since onset of heart attack matters a lot, and in fact that’s how doctors often gauge how far along a heart attack has been going. Either way, if folks tout “accuracy” and seemingly high numbers, check to see how they handled class imbalance. I’m pleased to note that a bunch of talented Stanford students I got to mentor in January-February did a great job spotting and handling this issue in an ML/AI exercise, as did some Columbia students as well. There’s hope for the future yet. https://lnkd.in/ezT2cyj6

𝐀𝐫𝐞 𝐲𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐯𝐢𝐬𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 𝐟𝐚𝐥𝐥𝐢𝐧𝐠 𝐬𝐡𝐨𝐫𝐭 𝐝𝐞𝐬𝐩𝐢𝐭𝐞 𝐡𝐢𝐠𝐡 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲? 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐩𝐢𝐭𝐟𝐚𝐥𝐥𝐬 𝐚𝐧𝐝 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐨 𝐨𝐯𝐞𝐫𝐜𝐨𝐦𝐞 𝐭𝐡𝐞𝐦. 𝐋𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐭𝐚𝐜𝐤𝐥𝐞 𝐢𝐦𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚, 𝐦𝐢𝐬𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐦𝐞𝐭𝐫𝐢𝐜𝐬, 𝐚𝐧𝐝 𝐞𝐧𝐡𝐚𝐧𝐜𝐞 𝐦𝐨𝐝𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬.  𝐈𝐦𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 → Underrepresented classes compared to others. → Leads to biased models favoring majority class. → Common in medical diagnosis, fraud detection, object recognition. → Requires resampling, data augmentation, class weight adjustment. → Metrics like Precision, Recall, F1-Score needed for evaluation.  𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐃𝐨𝐞𝐬𝐧'𝐭 𝐀𝐥𝐰𝐚𝐲𝐬 𝐆𝐢𝐯𝐞 𝐭𝐡𝐞 𝐂𝐨𝐫𝐫𝐞𝐜𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐀𝐛𝐨𝐮𝐭 𝐘𝐨𝐮𝐫 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐌𝐨𝐝𝐞𝐥 → Misleading with imbalanced datasets. → High accuracy may hide poor minority class performance. → Use Precision, Recall, F1-Score instead. → Confusion matrices provide detailed performance breakdown. → Comprehensive evaluation ensures effectiveness across classes.  𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐋𝐚𝐛𝐞𝐥 1 → Precision: True positives out of all positive predictions. → Recall: True positives out of all actual positives. → F1-Score: Harmonic mean of Precision and Recall. → Specificity: True negatives out of all actual negatives. → Balanced Accuracy: Average Recall across all classes.  𝐑𝐞𝐜𝐞𝐢𝐯𝐞𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐂𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐬𝐭𝐢𝐜 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 → ROC Curve: True Positive Rate vs. False Positive Rate. → AUC-ROC: Area summarizing model's discriminative ability. → Threshold Selection: Impacts True Positive and False Positive Rates. → Interpreting the Curve: Closer to top-left, better model. → Comparing Models: AUC-ROC allows straightforward performance comparison.  𝐌𝐮𝐥𝐭𝐢-𝐜𝐥𝐚𝐬𝐬 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 → One-vs-All Approach: Binary classification for each class. → Macro-Averaging: Average metrics treating all classes equally. → Micro-Averaging: Aggregate metrics, often favors majority classes. → Confusion Matrix: Visualize multi-class misclassifications. → Per-Class Metrics: Precision, Recall, F1-Score for each class.  𝐏𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 → Data Augmentation: Increase minority class samples through transformations. → Resampling Techniques: Balance dataset by oversampling or under sampling. → Class Weights Adjustment: Higher importance to minority class. → Advanced Algorithms: Models for imbalanced data, like Balanced Random Forest. → Ensemble Methods: Combine multiple models to improve performance. ♻️ Repost it to your network and follow Timothy Goebel for more. #computervision #machinelearning #datascience #modelperformance #aitechniques

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Consider a model you are building for a highly imbalanced classification problem where the minority class is very underrepresented. How could the bias-variance tradeoff be affected by the class imbalance, and what would you do to account for it? In a highly imbalanced classification problem, where the minority class is significantly underrepresented, the bias-variance tradeoff is directly affected by the model’s tendency to be biased toward the majority class. This often results in: 𝗛𝗶𝗴𝗵 𝗕𝗶𝗮𝘀: The model may oversimplify and predominantly predict the majority class, leading to underfitting, especially for the minority class. As a result, the model could have low variance but consistently poor performance on the minority class. 𝗛𝗶𝗴𝗵 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲: Alternatively, if the model overfits the minority class by learning patterns from a few examples, it may lead to high variance, especially on unseen data, and poor generalization. A few ways to combat this would be - 𝗥𝗲𝘀𝗮𝗺𝗽𝗹𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 : Oversampling the Minority Class or Reducing the number of examples from the majority class can help balance the dataset. 𝗖𝗹𝗮𝘀𝘀 𝗪𝗲𝗶𝗴𝗵𝘁𝘀 𝗔𝗱𝗷𝘂𝘀𝘁𝗺𝗲𝗻𝘁: Assigning higher weights to the minority class during model training ensures that the model does not ignore it. This approach helps reduce bias toward the majority class, leading to better performance on the minority class while maintaining a good tradeoff between bias and variance. 𝗙𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗥𝗲𝗰𝗮𝗹𝗹 𝗼𝗿 𝗙1-𝗦𝗰𝗼𝗿𝗲: Instead of accuracy, which can be misleading in imbalanced datasets, focusing on metrics like recall, F1-score, and precision-recall AUC helps evaluate the performance on the minority class. This ensures the model is properly tuned to handle imbalanced data without overfitting. 𝗔𝗱𝗷𝘂𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗧𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱: Instead of using the default threshold of 0.5, adjusting it to favor the minority class can improve recall for the underrepresented class without overfitting 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 down other techniques you could use ⬇ 𝗟𝗶𝗸𝗲 to see more such content. 𝗥𝗲𝗽𝗼𝘀𝘁 and see your own network grow

How do you deal with imbalanced data? If you don't have too much data and the imbalance is not too extreme, the typical way to deal with it is to simply reweigh the samples such that the loss function considers the positive and negative samples equally. When you have an overwhelming amount of negative samples, you may want to downsample them to minimize training latency. But not all samples are equal! At TikTok, for example, for their recommendation engine, they use a non-uniform negative sampling scheme they developed with the University of Connecticut: https://lnkd.in/gRsFSr2d. They proved that optimal sampling of the negative class is done when giving more weight to samples with a higher probability of being positive (Theorem 3). This means that it is better to keep samples that are confusing for a model. This way, the model focuses on learning how to separate true positive samples from negative samples that look like positive ones. Interestingly enough, this theorem also means sampling bias is a good thing! In ML applications, a model shows users some samples they are likely to engage with. When they don't engage with those, they become negative samples for the next training batch. That is sampling bias because only the samples with a high probability of engagement ever get shown to users, and they never get the opportunity to interact with the "lesser" samples, so we never get signals for those.     By sampling the data, we bias the probability estimates coming out of the model, and they become meaningless. The model is not calibrated anymore. To fix that, they came up with a correction of the likelihood function to generate unbiased estimates of the model parameters and, therefore, the probabilities (see eq 5). Practically, you follow this process to sample down negative samples: 1) Uniformly sample the negative class so that the data becomes balanced. 2) train a model with balanced data. They call it a "pilot" model. 3) predict the full data with that pilot model. You get an estimate of how much the model believes the sample is a positive one. 4) normalize that probability p by the average probability w and multiply by the sampling rate r: r * p / w 5) for each negative sample, pick a uniform random number u. If u < r * p / w, keep the sample; remove it otherwise. The greater p is, the more likely we will keep it 6) r * p / w is the sampling probability. When training the model or predicting, correct the log odds using that probability. Pretty simple process to follow! This is a simplified version of the more optimal approach, but they consider this approach satisfactory. -- 👉 Early-bird deal for my ML Fundamentals Bootcamp: https://lnkd.in/gasbhQSk -- #machinelearning #datascience #artificialintelligence

Excited to share our latest publication on handling class imbalance in binary classification! We've conducted a comprehensive study comparing three popular methods for handling class imbalance: - SMOTE - Class Weights Calibration - Decision Threshold Calibration Key highlights: - 9,000 experiments across 15 classifiers and 30 imbalanced datasets - All methods outperform the baseline (no intervention) - Decision Threshold Calibration emerges as the most consistent performer - Significant variability across datasets emphasizes the importance of testing multiple approaches for each specific problem Our findings offer valuable insights for data scientists and ML practitioners dealing with imbalanced datasets. We've made all our code, data, and results open-source to support further research and practical applications. Check out the full publication here: https://lnkd.in/dQ52DHj5 Ready Tensor is a platform for AI publications aimed at AI/ML developers and practitioners. Anyone can publish their work on our platform. We'll continue sharing insights on various AI and ML topics, so stay tuned! We would love to hear your thoughts and experiences with handling class imbalance. What strategies have you found effective? #MachineLearning #DataScience #ClassImbalance #SMOTE #DecisionThreshold #ClassWeights #BinaryClassification #OpenSource #ShareYourAI #ReadyTensor

10 years in fintech taught me a lot about customer churn — and that’s why this project hit differently. Churn is a challenge that can make or break a business. Whether it's in finance, retail, or telecom, losing customers hurts. But with the right tools and techniques, you can turn this challenge into an opportunity. Here's the latest in my ongoing series on Top 5 Data Science Projects every Data Scientist should explore: Churn Prediction in Telco using Random Forest, Decision Tree and XGBoost (Retail Concepts Applied) 💡 Why does churn prediction matter so much ? 🔄 Increase Retention: Identify at-risk customers and improve retention. 🎯 Optimize Marketing: Target at-risk customers with personalized offers. 💸 Boost CLV: Maximize customer lifetime value by retaining high-value customers. 🤝 Enhance CX: Drive proactive retention strategies for a better customer experience. 🚀 The Approach(This time Only highlights) 1. Label Encoding for Categorical Features: I chose Label Encoding over One-Hot Encoding to keep things efficient and avoid high-dimensional data, which could slow down computations and make the model less interpretable. 2. Handling Class Imbalance with SMOTE: In retail data, churned customers are often a smaller class. SMOTE (Synthetic Minority Oversampling Technique) was crucial to balance the data and make sure the model didn’t ignore the minority class. This led to more reliable predictions. 3. Model Training with Cross-Validation: I trained Decision Tree, Random Forest, and XGBoost models using 5-fold cross-validation. The results were clear: Random Forest came out on top with the best accuracy and generalization to unseen data. 4. Evaluation Beyond Accuracy: I didn’t stop at accuracy — precision, recall, and F1-score gave me a deeper understanding of how well the model was identifying at-risk customers. Key Learnings: a. Class Imbalance: It's critical to address imbalance to avoid skewed results. SMOTE helped level the playing field. b. Cross-Validation: It gave me confidence that the model wouldn’t overfit and would perform well on unseen data. Other Approaches can be Considered: Logistic Regression: Great for simpler problems with linear relationships. Neural Networks: Ideal for capturing complex, non-linear patterns (though not necessary here). 🔗 Check out the full project on GitHub: https://shorturl.at/w2BFv I’ve realized that Data Science isn’t just about crunching numbers — it’s about creating real-world impact.Predictive analytics is transforming industries like retail, finance, and beyond, and I’m here to share how. Follow me for more insights into the power of data science to drive innovation and change! #Retail #DataScience #MachineLearning #ChurnPrediction #SMOTE #RandomForest #XGBoost #DecisionTree #CrossValidation #CustomerRetention #PredictiveAnalytics #AI #Python #BusinessIntelligence #CustomerSuccess

*** Oversampling — Handling Imbalanced Data *** The Problem: Imbalanced Datasets Classes are often skewed in real-world datasets—think fraud detection (fraud vs. non-fraud) or rare disease prediction. One class dominates, making models biased toward the majority and blind to rare but crucial patterns. • Example: 98% of transactions are legitimate, 2% fraudulent. • Impact: A model may predict “legit” every time and still be 98% accurate—while missing every fraud case! The Solution: Oversampling Oversampling boosts the presence of the minority class in the training dataset. The goal is to balance the scales without losing vital characteristics of the data. Common Oversampling Techniques Oversampling helps ensure that minority classes in a dataset are adequately represented, reducing the chance that a model will ignore them. 1. Random Oversampling How it works: Replicates examples from the minority class at random until class balance is achieved. Pros: • Easy to implement • Preserves original data Cons: • Higher risk of overfitting since duplicates don’t add new information • May not capture diversity within the minority class 2. SMOTE (Synthetic Minority Over-sampling Technique) How it works: Generates new synthetic examples by interpolating between existing minority samples. Pros: • Reduces overfitting compared to random duplication • Creates more diverse synthetic data Cons: • Can create borderline or noisy samples • Assumes feature space is continuous and suitable for interpolation 3. ADASYN (Adaptive Synthetic Sampling) How it works: Like SMOTE, but it focuses more on generating synthetic data for minority examples that are harder to classify. Pros: • Targets complex regions where model performance struggles • Improves learning for difficult cases Cons: • Can increase noise and instability • Adds complexity to the training pipeline 4. Borderline-SMOTE How it works: SMOTE is applied mainly to samples near the decision boundary, where classification is most difficult. Pros: • Reinforces learning where misclassification risk is highest • Helps sharpen model boundaries Cons: • May generate ambiguous samples if boundary is unclear • Requires careful tuning Tailoring Technique to Context Choosing the correct method depends on: • Domain expertise: Some synthetic data might not make sense for your use case. • Data shape: High-dimensional or sparse data may struggle with SMOTE-style methods. • Model robustness: Some classifiers handle imbalance better than others, reducing the need for oversampling. --- B. Noted