What is Underfitting?
- Muiz As-Siddeeqi
- Oct 20
- 23 min read
Every machine learning model you build faces a hidden enemy that quietly destroys accuracy. It doesn't announce itself with flashy errors. It just sits there, producing mediocre predictions day after day, while you wonder why your expensive model can't learn what a child could figure out in minutes. That enemy is underfitting—and it's costing companies billions in lost opportunities, failed projects, and wasted resources.
TL;DR
Underfitting happens when models are too simple to capture data patterns, causing poor performance on both training and test data
High bias and low variance are the mathematical signatures of underfitting
Costs are massive: The global machine learning market reached $105.45 billion in 2024, but 43% of businesses struggle with model deployment issues (Statista, 2024)
Detection is straightforward: Look for consistently high training and test errors
Solutions exist: Increase model complexity, add features, reduce regularization, or train longer
Prevention matters more than cure: Proper model selection prevents 70% of underfitting cases (Machine Learning Mastery, 2019)
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in data. An underfit model performs poorly on both training data and new, unseen data because it cannot learn the relationships between input features and target outputs. High bias and low variance characterize underfitting, which typically results from overly simple model architectures, insufficient training time, or missing important features.
Table of Contents
What is Underfitting? Core Definition
Underfitting describes a model that cannot adequately capture the underlying structure of the data (Wikipedia, 2025). Picture trying to draw a curved line with only a straight edge—no matter how hard you try, you'll miss the true shape.
When a model underfits, it makes strong assumptions that oversimplify reality. Like overfitting, when a model is underfitted, it cannot establish the dominant trend within the data, resulting in training errors and poor performance of the model (IBM, 2025).
The mathematical signature is clear. Underfitting models have high bias and low variance (GeeksforGeeks, 2025). This means the model consistently makes the same mistakes across different datasets—not because of random fluctuations, but because it fundamentally lacks the capacity to represent the data.
The Everyday Analogy
Imagine a student preparing for a complex calculus exam by memorizing only basic arithmetic rules. No matter how many practice problems they see, they can't solve the actual test questions because their mental model is too simple. The student has "underfit" the material.
In machine learning, this happens when:
A linear model tries to fit non-linear data
A shallow neural network attempts to solve a complex pattern recognition task
Important features are excluded from the model
Training stops too early before the model learns enough
Underfitting in machine learning often occurs due to simplistic models, poor feature engineering or excessive regularization that overly restricts the model's flexibility (IBM, April 2025).
The Science Behind Underfitting: Bias and Variance
Understanding underfitting requires grasping the bias-variance tradeoff—one of machine learning's foundational concepts.
Bias is the error that happens when a machine learning model is too simple and doesn't learn enough details from the data (GeeksforGeeks, 2025). High bias means the model makes strong, incorrect assumptions about the data structure.
Think of bias as stubbornness. A highly biased model refuses to acknowledge complexity, insisting the world follows simple rules even when evidence suggests otherwise.
What is Variance?
Variance is the error that happens when a machine learning model learns too much from the data, including random noise (GeeksforGeeks, 2025). High variance means the model changes dramatically with small changes in training data.
The Tradeoff
As the number of tunable parameters in a model increase, it becomes more flexible, and can better fit a training data set. However, for more flexible models, there will tend to be greater variance to the model fit each time we take a set of samples to create a new training data set (Wikipedia, 2025).
Recent research confirms this fundamental principle. Ensemble methods such as Random Forest, Gradient Boosting and XGBoost consistently achieve the best tradeoff between bias and variance, resulting in the lowest overall error (Ranglani, December 2024).
Key Insight: Underfitting sits on the high-bias, low-variance end of this spectrum. The model's predictions are consistently wrong in the same way, regardless of which training samples you use.
Mathematical Decomposition
The expected prediction error can be decomposed into three components:
Total Error = Irreducible Error + Bias² + Variance
For underfit models:
Irreducible Error: Fixed (inherent data noise)
Bias²: HIGH (model is too simple)
Variance: LOW (predictions are consistent, just consistently wrong)
Underfitting vs Overfitting vs Good Fit
To truly understand underfitting, you must see where it sits in the model complexity spectrum.
Characteristic | Underfitting | Good Fit | Overfitting |
Training Error | High | Low | Very Low |
Test Error | High | Low | High |
Bias | High | Balanced | Low |
Variance | Low | Balanced | High |
Model Complexity | Too simple | Optimal | Too complex |
Generalization | Poor | Excellent | Poor |
Real-World Analogy | Student who didn't study enough | Student who understood concepts | Student who memorized answers |
Visual Understanding
An underfit model doesn't even make good predictions on the training data. If an overfit model is like a product that performs well in the lab but poorly in the real world, then an underfit model is like a product that doesn't even do well in the lab (Google Machine Learning, 2025).
Example: House Price Prediction
Imagine predicting house prices:
Underfitting: Using only one feature (square footage) with a simple linear model. The model assumes price = constant × square footage, ignoring location, age, condition, and neighborhood. Result: Poor predictions everywhere.
Good Fit: Using 15 relevant features (square footage, location, age, bedrooms, bathrooms, school district, crime rate, etc.) with an appropriately complex model. Result: Accurate predictions on new houses.
Overfitting: Using 200 features (including irrelevant ones like "paint color of front door") with an overly complex model that learns noise. Result: Perfect predictions on training houses, terrible predictions on new houses.
Real-World Impact and Statistics
Underfitting isn't just a theoretical problem. It has measurable costs across industries.
Market Size and Growth
The current global market size of machine learning is $105.45 billion and can grow to $568.32 billion by 2031 (MindInventory, 2025). Yet deployment challenges, including model performance issues, plague the industry.
43% of businesses face challenges in scaling up when adopting AI and machine learning (DemandSage, May 2025). Model quality issues—including underfitting—contribute significantly to these failures.
Adoption Challenges
42% of enterprise-scale companies surveyed report using AI in their business, and an additional 40% of respondents say they are exploring AI (ITTransition, 2024). However, the gap between exploration and successful deployment often involves model performance problems.
72% of IT leaders mention AI skills as one of the crucial gaps that needs to be addressed urgently (ITTransition, 2024). This skills gap means many practitioners don't recognize underfitting when they see it.
Financial Impact
Machine learning and artificial intelligence industry advancements are expected to increase the GDP by 14% (DemandSage, May 2025). But only models that actually work deliver this value—underfit models waste resources without generating returns.
In banking specifically, automation of middle-office tasks using ML and AI could save North American banks up to $70 billion in 2025 (Scoop Market, March 2025). Underfit credit scoring or fraud detection models fail to capture these savings.
Model Accuracy Benchmarks
Recent data shows what's possible with properly fit models:
The average top-5 accuracy for large vision-language models reached 97.3% in 2025 (SQ Magazine, 2025). ML models for credit scoring achieved 91% AUC (Area Under Curve) performance, reducing false positives in loan rejections (SQ Magazine, 2025).
Underfit models fall far short of these benchmarks, sometimes achieving accuracy barely better than random guessing.
Case Studies: When Underfitting Costs Real Money
Real documented examples show underfitting's impact across industries.
Case Study 1: Medical Diagnosis Model (IBM, April 2025)
Context: A machine learning model is trained to classify medical images as "healthy" or "diseased" on a small data set (IBM, April 2025).
The Problem: The initial model used a simple linear classifier with insufficient features, assuming disease presence could be determined by basic pixel intensity patterns alone.
Outcome: The model showed high error rates on both training and test data. It couldn't distinguish between healthy tissue variations and actual disease markers because the model architecture was too simple to capture subtle visual patterns that indicate disease.
Lesson: Medical imaging requires complex models capable of recognizing non-linear patterns. Simple models consistently fail, regardless of dataset size.
Fix Applied: Switching to a convolutional neural network with multiple layers allowed the model to learn hierarchical features from the images, dramatically improving performance.
Case Study 2: Healthcare Readmission Prediction (Meegle, 2024)
Context: A healthcare startup developed a model to predict patient readmission rates. The model performed exceptionally well on training data but failed to generalize to new hospitals due to overfitting to specific demographic patterns (Meegle, 2024).
The Problem: The initial attempt to fix this by oversimplifying the model created the opposite problem—underfitting. The simplified model couldn't account for legitimate variations in patient populations.
Outcome: The underfit version performed poorly across all hospitals, missing critical risk factors that varied by location.
Lesson: Simplification isn't always the answer. Models need enough complexity to capture real variation while avoiding noise.
Solution: Feature engineering and regularization provided better balance than pure simplification.
Case Study 3: Financial Trading Algorithm (Meegle, 2024)
Context: A trading algorithm trained on noisy historical data made inaccurate predictions, leading to significant financial losses for an investment firm (Meegle, 2024).
The Problem: After initial losses from overfitting, the firm overcorrected by using an overly simple model that couldn't capture market complexity.
Outcome: The underfit model failed to recognize profitable trading opportunities because it assumed linear relationships in inherently non-linear markets.
Financial Impact: Estimated losses in the millions before the problem was identified and corrected.
Lesson: Financial markets exhibit complex, non-linear behavior. Models must have sufficient capacity to learn these patterns while managing noise.
Case Study 4: Image Recognition in Varying Conditions (Meegle, 2024)
Context: An image recognition model overfitted to specific lighting conditions in training data, resulting in poor performance in real-world applications (Meegle, 2024).
The Problem: The initial fix attempted to make the model more generalizable by reducing complexity, creating an underfit model that couldn't recognize objects in any lighting condition.
Outcome: The simplified model performed poorly across all scenarios because it lacked the capacity to learn robust object features.
Solution: Data augmentation with diverse lighting conditions, combined with appropriate model complexity, solved both the overfitting and underfitting issues.
How to Detect Underfitting
Early detection saves time and resources. Here are proven methods to spot underfitting quickly.
Method 1: Training vs Test Error Analysis
The Most Reliable Indicator
Underfitting occurs when a model is too simple and is unable to properly capture the patterns and relationships in the data (Built In, March 2024).
What to Look For:
Both training and test errors are high
Small gap between training and test errors (both are bad)
Errors don't decrease significantly with more training
Example metrics that signal underfitting:
Training accuracy: 62%
Test accuracy: 60%
Gap: 2% (small, but both numbers are low)
Method 2: Learning Curves
Learning curves plot the training and validation loss of a sample of training examples by incrementally adding new training examples (Medium, July 2023).
Underfitting Signatures in Learning Curves:
Learning curve of an underfit model shows high training loss that remains high and doesn't decrease much even with more training examples. The validation loss also stays high and doesn't improve (Medium, July 2023).
What You'll See:
Both curves start high
Both curves stay high
Curves run nearly parallel
Minimal improvement with more data
Contrast with Good Fit:
Training loss starts high, decreases steadily
Validation loss decreases and stabilizes
Small final gap between curves
Curves converge to low error
Method 3: Cross-Validation Scores
Cross-validation results can highlight overfitting or underfitting issues. Underfitting may be indicated by poor performance on both training and testing data (Knowledge Academy, 2024).
What to Check:
All fold scores are consistently low
Low variance across folds (but consistently poor)
No fold performs well
Method 4: Residual Analysis
For regression problems, examine prediction residuals (actual - predicted values).
Underfitting Residual Patterns:
Systematic patterns in residuals (not random)
Residuals show clear structure when plotted
Residuals correlate with certain feature values
Large residual magnitudes across the board
Method 5: Visual Inspection (For 2D Data)
For simple datasets, plot the model's predictions against actual data.
What Underfit Models Look Like:
Model line/curve is too simple for the data
Clear systematic deviations from data points
Model misses obvious patterns visible to human eye
Example: Fitting a straight line to data that clearly curves produces large, systematic errors.
Detection Checklist
✓ Training error > 70% (for classification) or high MSE (for regression)
✓ Test error similar to training error
✓ Learning curves stay flat and high
✓ Cross-validation shows consistently poor performance
✓ Residuals show clear patterns
✓ Visual inspection shows oversimplification
✓ Model uses very few features or parameters
If you check 4+ boxes, you likely have underfitting.
How to Fix Underfitting: Seven Proven Solutions
Once you've detected underfitting, these methods can fix it.
Solution 1: Increase Model Complexity
The Primary Fix
With any model, specific features are used to determine a given outcome. If there are not enough predictive features present, then more features or features with greater importance, should be introduced (IBM, 2025).
For Neural Networks:
Add more hidden layers
Increase neurons per layer
Use more complex activation functions
For Decision Trees:
Increase maximum depth
Reduce minimum samples per leaf
Add more trees (for Random Forests)
For Linear Models:
Add polynomial features
Include interaction terms
Use kernel methods
Example:
Before: Linear regression with 3 features
After: Polynomial regression (degree 3) with 3 features = 10 parameters
Result: Model can now capture non-linear relationships
Solution 2: Add More Features
Feature Engineering Saves Models
For example, in a neural network, you might add more hidden neurons or in a random forest, you may add more trees. This process will inject more complexity into the model, yielding better training results (IBM, 2025).
Strategies:
Domain knowledge: Add features experts consider important
Automated feature generation: Create polynomial, logarithmic, or exponential transformations
Feature crosses: Combine existing features (e.g., bedroom_count × square_footage)
Temporal features: For time-series data, add lagged values, rolling averages
External data: Incorporate additional relevant datasets
Real Example: Predicting taxi demand improved significantly when weather data, local events, and holiday indicators were added to time and location features alone.
Solution 3: Reduce or Remove Regularization
Too Much of a Good Thing
By extending the duration of training, it can be avoided. However, it is important to cognizant of overtraining, and subsequently, overfitting (IBM, 2025).
Although it's used to resolve overfitting, L1 regularization can actually cause model underfitting based on the lambda value. A very large value will make coefficients zero. This means the model will become underfit (Built In, October 2024).
Action Steps:
Reduce regularization parameter (λ or alpha)
Start with λ = 0, then gradually increase only if overfitting appears
Try less aggressive regularization (L2 instead of L1)
Remove dropout layers (or reduce dropout rate)
Reduce early stopping patience
Solution 4: Train Longer
Patience Pays Off
Contributing factors include overly simplified model structures, insufficient training duration, excessive regularization, and omission of relevant features (New Math Data, July 2025).
How Long is Long Enough?
Monitor training loss—it should decrease
Use early stopping based on validation loss, not fixed epochs
For deep learning: may need hundreds or thousands of epochs
For traditional ML: convergence typically occurs faster
Warning Signs of Insufficient Training:
Training loss still decreasing when training stops
Model performance improves if you train longer
Learning curves haven't flattened
Solution 5: Use More Training Data
When Possible, Scale Up
When the training set has far fewer observations than variables, this may lead to underfitting or low bias machine learning models (VitalFlux, August 2024).
Data Collection Strategies:
Gather more examples of existing classes
Use data augmentation (for images, text, audio)
Generate synthetic data (if appropriate)
Combine datasets from multiple sources
Use transfer learning from pre-trained models (requires less data)
Threshold Guidelines:
Simple models: minimum 10x samples relative to parameters
Complex models: minimum 100x samples relative to parameters
Deep learning: thousands to millions of samples
Solution 6: Choose a More Appropriate Model
Right Tool for the Job
Linear regression biases the model to learn linear relationships in data, so linear regression models will underfit to non-linear datasets. Similarly, under-parameterized models, i.e. those with few parameters, are likely to bias the model towards simplistic relationships (Domino Data Lab, June 2025).
Model Selection Guide:
For Linear Relationships: Linear regression, logistic regression
For Non-Linear Relationships: Polynomial regression, SVM with RBF kernel, neural networks
For Complex Patterns: Deep neural networks, ensemble methods (Random Forest, XGBoost)
For Image Data: Convolutional Neural Networks (CNNs)
For Sequential Data: RNNs, LSTMs, Transformers
For Tabular Data: Gradient Boosting (XGBoost, LightGBM, CatBoost)
Solution 7: Remove or Adjust Constraints
Free Your Model
Sometimes underfitting occurs because constraints are too tight:
Common Constraints to Review:
Maximum tree depth in decision trees
Minimum samples per split/leaf
Number of components in dimensionality reduction
Maximum iterations in optimization
Learning rate (too low prevents convergence)
Industry-Specific Applications
Underfitting manifests differently across industries. Here's what to watch for.
Healthcare and Life Sciences
Challenge: The healthcare industry is projected to hold the largest market share in machine learning by 2025 (A3Logics, February 2025).
Common Underfitting Scenarios:
Disease diagnosis models with insufficient features
Drug efficacy predictions using overly simple models
Patient readmission models that ignore comorbidities
Solution Approach:
Include comprehensive patient history
Use ensemble methods to capture complex interactions
Incorporate genetic, lifestyle, and environmental factors
Success Metric: ML can help achieve up to 95% accuracy in predicting COVID-19-related physiological deterioration (DemandSage, May 2025)—but only with properly fit models.
Challenge: The second largest market in the machine learning industry is the finance industry, with a market share of 15.42% (DemandSage, May 2025).
Underfitting Red Flags:
Credit scoring models that use only 3-4 features
Fraud detection with high false negative rates
Trading algorithms missing profitable opportunities
Best Practices:
Capture non-linear relationships in market data
Include behavioral features beyond basic transactions
Use gradient boosting for tabular financial data
Real Results: Some banks have reported a 98% reduction in new account frauds by implementing machine learning models (A3Logics, February 2025).
Challenge: The manufacturing industry is responsible for nearly a fifth (18.88%) of the global machine learning market (AIPRM, July 2024).
Underfitting Issues:
Quality control models missing defect patterns
Predictive maintenance with high false negatives
Supply chain optimization ignoring seasonal patterns
Solutions:
Use computer vision with sufficient model depth
Include temporal features for time-dependent failures
Ensemble methods for complex multivariate data
Retail and E-commerce
Underfitting Symptoms:
Recommendation engines suggesting irrelevant products
Demand forecasting with persistent errors
Customer churn models missing at-risk segments
Key Interventions:
Add behavioral features (click patterns, browsing history)
Include temporal trends and seasonality
Use collaborative filtering with sufficient factors
Performance Target: ML-based inventory optimization systems led to a 23% average reduction in stockouts for large retail companies (SQ Magazine, 2025).
Common Mistakes and Pitfalls
Even experienced practitioners make these errors.
Mistake 1: Assuming Simplicity is Always Better
The Error: Applying Occam's Razor too aggressively, choosing the simplest possible model without testing if it actually fits the data.
Why It Happens: Misunderstanding the bias-variance tradeoff or premature optimization for interpretability.
The Fix: Start with a model complex enough to potentially fit the data, then simplify if needed.
Mistake 2: Insufficient Feature Engineering
The Error: Feeding raw features to the model without transformation, combination, or augmentation.
Impact: Model cannot learn relationships that don't exist in the raw feature space.
The Fix: Invest time in feature engineering before modeling. Domain expertise helps enormously.
Mistake 3: Stopping Training Too Early
The Error: Ending training after a fixed number of iterations without monitoring convergence.
Why It Happens: Impatience or computational cost concerns.
The Fix: Use early stopping based on validation performance, not arbitrary epochs.
Mistake 4: Over-Regularization
The Error: Excessive regularization are used to prevent the overfitting, which constraint the model to capture the data well (GeeksforGeeks, 2025).
The Fix: Start with no regularization, then add only as much as needed to prevent overfitting.
Mistake 5: Ignoring Data Scaling
The Error: Not normalizing or standardizing features, causing certain features to dominate unfairly.
Impact: Features are not scaled can lead to underfitting (GeeksforGeeks, 2025).
The Fix: Always scale features appropriately (StandardScaler, MinMaxScaler, or RobustScaler).
Mistake 6: Using the Wrong Model Family
The Error: Trying to fit non-linear data with linear models, or time-series data with static models.
The Fix: Match model family to problem structure—check assumptions first.
Mistake 7: Not Enough Data
The Error: The size of the training dataset used is not enough (GeeksforGeeks, 2025).
The Fix: Collect more data, use data augmentation, or choose simpler models appropriate for small datasets.
Myths vs Facts
Myth 1: "Underfitting is Better Than Overfitting"
Fact: Both are equally bad, just in different ways. Underfit models are useless in production. They provide no value because they can't make accurate predictions on any data—training or test.
Myth 2: "More Data Always Fixes Underfitting"
Fact: More data helps only if the model has sufficient capacity. Adding data to an overly simple model won't help—the model still can't learn complex patterns no matter how many examples you show it.
Myth 3: "Underfitting is Easy to Detect"
Fact: While training/test error comparisons help, subtle underfitting can hide behind acceptable but suboptimal performance. A model achieving 75% accuracy might seem okay, but the data might support 90% with proper fitting.
Myth 4: "Linear Models Always Underfit"
Fact: Linear models underfit only when the true relationship is non-linear. For truly linear relationships, linear models provide the best fit with optimal generalization.
Myth 5: "Cross-Validation Prevents Underfitting"
Fact: Validation (CV or holdout) might help you detect under- or over-fitness, but it doesn't improve or avoid either one (Cross Validated, 2022). Cross-validation detects underfitting; it doesn't fix it.
Myth 6: "Deep Learning Never Underfits"
Fact: Complex models such as neural networks may underfit to data if they are not trained for long enough or are trained with poorly chosen hyperparameters (Domino Data Lab, June 2025).
Myth 7: "Regularization Only Helps, Never Hurts"
Fact: If lambda is very large then it will add too much weight and lead to underfitting (Built In, October 2024). Excessive regularization can cause underfitting.
Tools and Frameworks
Modern tools make detecting and fixing underfitting easier.
For Model Training
Scikit-learn (Python)
Built-in cross-validation: cross_val_score, KFold
Learning curve visualization: learning_curve
Model selection: GridSearchCV, RandomizedSearchCV
Wide range of models with hyperparameter control
TensorFlow and Keras (Python)
Deep learning frameworks with flexible architectures
Callbacks for monitoring training: ModelCheckpoint, EarlyStopping
TensorBoard for visualization
Pre-trained models for transfer learning
PyTorch (Python)
Research-friendly deep learning framework
Dynamic computational graphs
Extensive model zoo
Strong community support
XGBoost / LightGBM / CatBoost
Gradient boosting libraries
Excellent performance on tabular data
Built-in cross-validation
Handle complex non-linear patterns
For Visualization and Diagnosis
Matplotlib / Seaborn (Python)
Create learning curves
Plot training vs validation error
Visualize residuals
Display confusion matrices
Plotly (Python/JavaScript)
Interactive plots
Dashboard creation
Real-time monitoring
Weights & Biases / MLflow
Experiment tracking
Hyperparameter comparison
Model registry
Performance monitoring over time
For Cross-Validation
K-fold cross-validation is the standard approach. As a general rule, most authors and empirical evidence suggest that 5 or 10-fold cross validation should be preferred (Scikit-learn, 2025).
Implementation:
from sklearn.model_selection import cross_val_score, KFold
kfold = KFold(n_splits=10, shuffle=True, random_state=42)
scores = cross_val_score(model, X, y, cv=kfold, scoring='accuracy')
print(f"Mean Accuracy: {scores.mean():.2f}")
print(f"Std Deviation: {scores.std():.2f}")For Learning Curves
from sklearn.model_selection import learning_curve
train_sizes, train_scores, val_scores = learning_curve(
model, X, y, cv=5, scoring='accuracy',
train_sizes=np.linspace(0.1, 1.0, 10)
)Future Trends
The fight against underfitting evolves with technology.
AutoML and Neural Architecture Search
According to Gartner, Gen AI will augment the human workforce in 90% of companies globally by 2025 (Encord, August 2024).
Impact on Underfitting:
Automated model selection reduces human error
Architecture search finds optimal complexity
Hyperparameter tuning becomes more efficient
Google's acquisition of DataRobot: In January 2024, Google acquired DataRobot, a leading provider of automated machine learning (AutoML) solutions, for an estimated $5 billion (Scoop Market, March 2025).
Transfer Learning and Pre-trained Models
You can utilize modeling techniques such as transfer learning to take pre-trained models that have already been trained on large datasets and fine-tune them to your own, unique data (Domino Data Lab, June 2025).
Benefits:
Reduces risk of underfitting with small datasets
Captures complex patterns without massive data requirements
Enables sophisticated models in data-scarce domains
Edge AI and Model Compression
Gartner predicts that over 55% of deep neural networks will analyze data at the source by 2025 (Encord, August 2024).
Challenge: Compressed models risk underfitting if compression is too aggressive.
Solution: New techniques like knowledge distillation maintain performance while reducing size.
As models become more complex to avoid underfitting, interpretability becomes crucial. ML is embedded in 72% of ERP systems, often automating invoice processing and vendor performance tracking (SQ Magazine, 2025).
FAQ
Q1: What's the difference between underfitting and overfitting?
Underfitting occurs when a model is too simple to capture data patterns, performing poorly on both training and test data. Overfitting occurs when a model is too complex, learning noise instead of signal, performing well on training data but poorly on test data. High bias and low variance are good indicators of underfitting (IBM, 2025), while overfitting shows low bias and high variance.
Q2: Can underfitting and overfitting occur simultaneously?
Not in the same model. They represent opposite ends of the model complexity spectrum. However, different parts of a model might exhibit different behaviors, or a model might underfit some patterns while overfitting others.
Q3: How do I know if my model is underfit or just has bad data?
Examine data quality first—check for errors, missing values, or class imbalance. If data quality is good but both training and test errors remain high, and increasing model complexity improves performance, you likely have underfitting. If data is genuinely noisy or has no predictable patterns, even complex models will struggle.
Q4: Does more training data fix underfitting?
Sometimes. If the model lacks statistical power due to insufficient samples, more data helps. But if the model is fundamentally too simple—like fitting a line to a curve—more data won't help. The model still can't learn the pattern because it lacks the capacity, not the examples.
Q5: What's the fastest way to check for underfitting?
Compare training and test errors. If both are high and similar, you likely have underfitting. For a quick visual check with small datasets, plot predictions against actual values—if the model's line/surface is obviously too simple for the data pattern, you're underfit.
Q6: Can regularization cause underfitting?
Yes. A very large value will make coefficients zero. This means the model will become underfit (Built In, October 2024). Regularization penalizes model complexity, which helps prevent overfitting but can cause underfitting if the penalty is too severe.
Q7: How does underfitting relate to the bias-variance tradeoff?
Underfitting sits at the high-bias, low-variance end of the spectrum. The model makes consistent but wrong predictions because it's too simple to capture the true relationship. Adding complexity reduces bias but increases variance—the art is finding the sweet spot.
Q8: What's the minimum dataset size to avoid underfitting?
There's no universal answer—it depends on problem complexity and model type. A rough guideline: have at least 10x more training samples than model parameters. For deep learning, thousands to millions of examples may be needed. For simple linear regression, hundreds might suffice.
Q9: Can feature selection cause underfitting?
Yes, if you remove important features. While feature selection helps prevent overfitting and improve computational efficiency, removing features that carry genuine predictive information will cause underfitting. Use domain knowledge and careful evaluation to guide feature selection.
Q10: How do ensemble methods relate to underfitting?
Ensemble methods such as Random Forest, Gradient Boosting and XGBoost consistently achieve the best tradeoff between bias and variance (Ranglani, December 2024). They reduce both bias (by using multiple complex models) and variance (by averaging predictions), making them resistant to both underfitting and overfitting.
Q11: Does k-fold cross-validation fix underfitting?
No. The purpose of validation is to evaluate model performance after fitting, not to make the model more or less fit (Cross Validated, 2022). Cross-validation helps detect underfitting but doesn't fix it. You must still adjust the model itself.
Q12: What metrics best detect underfitting?
For classification: accuracy, precision, recall, F1-score (all should be low for both train/test). For regression: MSE, RMSE, MAE, R² (high error metrics, low R²). The key indicator: training and test metrics are similarly poor.
Q13: Can transfer learning prevent underfitting?
Often yes, especially with small datasets. Pre-trained models have already learned complex patterns from large datasets. Fine-tuning these models on your specific task can achieve good performance even with limited data, avoiding the underfitting that would occur if training from scratch.
Q14: How do I avoid underfitting in production?
Monitor model performance continuously. Set minimum performance thresholds. When performance degrades, investigate whether the model has become too simple for evolving data patterns. Retrain regularly with updated architectures if needed.
Q15: What's the relationship between underfitting and model interpretability?
There's a tradeoff. Simple, interpretable models (linear regression, decision trees) risk underfitting complex data. Complex, accurate models (deep neural networks) sacrifice interpretability. Modern XAI techniques help explain complex models, reducing the need to choose between accuracy and interpretability.
Key Takeaways
Underfitting means your model is too simple to capture real patterns—it performs poorly on both training and test data.
High bias and low variance are the mathematical fingerprints of underfitting—consistent but consistently wrong predictions.
Detection is straightforward: Look for high training error, high test error, flat learning curves, and poor cross-validation scores.
The fix requires increasing model capacity: Add features, increase complexity, train longer, or reduce regularization.
Industry impact is massive: With machine learning markets exceeding $100 billion globally, underfit models waste billions in potential value.
Every industry faces underfitting risks: Healthcare, finance, manufacturing, and retail all struggle with models that are too simple for complex data.
Prevention beats cure: Proper model selection and feature engineering prevent 70% of underfitting cases before they occur.
Tools make it easier: Scikit-learn, TensorFlow, PyTorch, and XGBoost provide built-in methods for detection and correction.
The bias-variance tradeoff is fundamental: Understanding this concept is essential for navigating between underfitting and overfitting.
Future trends favor sophistication: AutoML, transfer learning, and pre-trained models make avoiding underfitting easier than ever.
Actionable Next Steps
Audit Your Current Models
Calculate training and test errors for all production models
Create learning curves for each model
Document which models show underfitting symptoms
Implement Systematic Detection
Set up k-fold cross-validation for model evaluation
Create dashboard to monitor training vs test performance
Establish minimum performance thresholds
Build a Fix Priority List
Rank underfit models by business impact
Start with models affecting revenue or risk
Document baseline performance before changes
Apply Solutions Methodically
For each underfit model, try these in order:
Add relevant features based on domain knowledge
Increase model complexity one step at a time
Reduce regularization if currently applied
Collect more training data if possible
Switch to more appropriate model family
Validate Improvements
Re-run cross-validation after each change
Verify test set performance improves
Ensure you haven't created overfitting
Document what worked and what didn't
Establish Prevention Protocols
Create model selection guidelines for your team
Require learning curve analysis for new models
Build feature engineering checklist
Train team on bias-variance tradeoff
Monitor Continuously
Set up automated performance tracking
Alert when models degrade
Schedule quarterly model audits
Update models as data evolves
Invest in Tools and Skills
Adopt modern ML frameworks (scikit-learn, PyTorch)
Train team on cross-validation techniques
Learn AutoML for model selection
Study ensemble methods
Share Knowledge
Document lessons learned from fixing underfitting
Create internal case studies
Build team expertise through practice
Contribute to industry knowledge
Plan for the Future
Evaluate AutoML tools for your organization
Investigate transfer learning opportunities
Stay current with research on bias-variance tradeoff
Prepare for edge AI deployment challenges
Glossary
Bias: Error from incorrect assumptions in the model. High bias means the model is too simple and consistently misses patterns.
Variance: Error from sensitivity to training data fluctuations. High variance means the model changes dramatically with different training samples.
Cross-Validation: Technique for assessing model performance by training and testing on different data subsets multiple times.
Feature Engineering: Creating new input features from existing ones to help models learn patterns more effectively.
Generalization: A model's ability to perform well on new, unseen data, not just training data.
Hyperparameter: Configuration setting chosen before training (e.g., learning rate, tree depth) that controls model behavior.
Learning Curve: Plot showing model performance (training and validation error) as training data size or iterations increase.
Model Complexity: Measure of a model's capacity to fit data, related to number of parameters or architectural sophistication.
Overfitting: When a model learns training data too well, including noise, causing poor performance on new data.
Regularization: Technique that adds penalties to model complexity to prevent overfitting.
Test Error: Model's prediction error on data not used during training; measures generalization performance.
Training Error: Model's prediction error on data used during training; measures fit to training data.
Underfitting: When a model is too simple to capture patterns in data, causing poor performance on both training and test data.
Sources & References
Wikipedia. (2025, October). "Overfitting." https://en.wikipedia.org/wiki/Overfitting
GeeksforGeeks. (2025, January 27). "ML | Underfitting and Overfitting." https://www.geeksforgeeks.org/machine-learning/underfitting-and-overfitting-in-machine-learning/
Machine Learning Mastery. (2019, August 12). "Overfitting and Underfitting With Machine Learning Algorithms." https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
Domino Data Lab. (2025, June 10). "What is Underfitting with ML Models?" https://domino.ai/data-science-dictionary/underfitting
IBM. (2025, October). "What Is Underfitting?" https://www.ibm.com/think/topics/underfitting
Google for Developers. (2025). "Overfitting | Machine Learning." https://developers.google.com/machine-learning/crash-course/overfitting/overfitting
New Math Data. (2025, July 8). "The Problem of Underfitting in Machine Learning." https://newmathdata.com/blog/what-is-underfitting-machine-learning-how-to-test-minimize/
IBM. (2025, April 16). "What Is Overfitting vs. Underfitting?" https://www.ibm.com/think/topics/overfitting-vs-underfitting
Built In. (2024, March 7). "Overfitting vs. Underfitting Explained." https://builtin.com/articles/overfitting-vs-underfitting
ITTransition. (2024). "The Ultimate List of Machine Learning Statistics for 2025." https://www.itransition.com/machine-learning/statistics
AIPRM. (2024, July 17). "Machine Learning Statistics 2024." https://www.aiprm.com/machine-learning-statistics/
Statista. (2025). "Machine Learning - Worldwide." https://www.statista.com/outlook/tmo/artificial-intelligence/machine-learning/worldwide
A3Logics. (2025, February 15). "Machine Learning Statistics That Matter in 2025." https://www.a3logics.com/blog/machine-learning-statistics/
DemandSage. (2025, May 12). "70+ Machine Learning Statistics 2025: Industry Market Size." https://www.demandsage.com/machine-learning-statistics/
Encord. (2024, August 19). "2024 Machine Learning Trends & Statistics." https://encord.com/blog/machine-learning-trends-statistics/
SQ Magazine. (2025). "Machine Learning Statistics 2025: Market Size, Adoption, Trends." https://sqmagazine.co.uk/machine-learning-statistics/
Grand View Research. (2025). "Machine Learning Market Size & Share | Industry Report 2030." https://www.grandviewresearch.com/industry-analysis/machine-learning-market
Scoop Market. (2025, March 14). "Machine Learning Statistics and Facts (2025)." https://scoop.market.us/top-machine-learning-statistics/
MindInventory. (2025). "Machine Learning Statistics 2025: Market Growth, Adoption, ROI, Jobs, and Future Trends." https://www.mindinventory.com/blog/machine-learning-statistics/
H2O.ai. (2025). "What is Bias-Variance Tradeoff?" https://h2o.ai/wiki/bias-variance-tradeoff/
Ranglani, Hardev. (2024, December 5). "Empirical Analysis Of The Bias-Variance Tradeoff Across Machine Learning Models." Machine Learning and Applications: An International Journal (MLAIJ) Vol.11, No. 4.
Nature Human Behaviour. (2018, February 5). "A bias–variance trade-off governs individual differences in on-line learning in an unpredictable environment." https://www.nature.com/articles/s41562-018-0297-4
ScienceDirect. (2022, October 21). "Bias-variance tradeoff in machine learning: Theoretical formulation and implications to structural engineering applications." https://www.sciencedirect.com/science/article/abs/pii/S2352012422009018
Wikipedia. (2025). "Bias–variance tradeoff." https://en.wikipedia.org/wiki/Bias–variance_tradeoff
VitalFlux. (2024, August 24). "Overfitting & Underfitting in Machine Learning." https://vitalflux.com/overfitting-underfitting-concepts-interview-questions/
Medium. (2024, February 20). "Assessing the Performance (Types and Sources of Error) in Machine Learning." https://medium.com/analytics-vidhya/assessing-the-performance-types-and-sources-of-error-in-machine-learning-e5d28b71da6b
VitalFlux. (2024, August 25). "Reducing Overfitting vs Models Complexity: Machine Learning." https://vitalflux.com/model-complexity-overfitting-in-machine-learning/
FlowHunt. (2025). "Training Error." https://www.flowhunt.io/glossary/training-error/
The Knowledge Academy. (2024). "K Fold Cross Validation In Machine Learning: Explained In Detail." https://www.theknowledgeacademy.com/blog/k-fold-cross-validation-in-machine-learning/
Tilburg Science Hub. (2024). "Detecting Overfitting with K-Fold Cross-Validation." https://tilburgsciencehub.com/topics/analyze/machine-learning/supervised/k-fold_crossvalidation/
GeeksforGeeks. (2025, April 22). "K- Fold Cross Validation in Machine Learning." https://www.geeksforgeeks.org/machine-learning/k-fold-cross-validation-in-machine-learning/
GeeksforGeeks. (2025, July 23). "How K-Fold Prevents overfitting in a model?" https://www.geeksforgeeks.org/how-k-fold-prevents-overfitting-in-a-model/
Scikit-learn. (2025). "3.1. Cross-validation: evaluating estimator performance." https://scikit-learn.org/stable/modules/cross_validation.html
Cross Validated. (2022, June 29). "Does cross validation reduce underfitting?" https://stats.stackexchange.com/questions/580386/does-cross-validation-reduce-underfitting
Towards Data Science. (2025, March 5). "How to Mitigate Overfitting with K-Fold Cross-Validation." https://towardsdatascience.com/how-to-mitigate-overfitting-with-k-fold-cross-validation-518947ed7428/
Medium. (2023, July 7). "Learning Curve to identify Overfitting and Underfitting in Machine Learning." https://medium.com/data-science/learning-curve-to-identify-overfitting-underfitting-problems-133177f38df5
Built In. (2024, October 3). "L1 and L2 Regularization Methods, Explained." https://builtin.com/data-science/l2-regularization
Towards Data Science. (2025, March 5). "Ridge and Lasso Regression: L1 and L2 Regularization." https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b/
PMC. (2011). "Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features." https://pmc.ncbi.nlm.nih.gov/articles/PMC3224215/
GeeksforGeeks. (2019, May 23). "Regularization in Machine Learning." https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/
Meegle. (2024). "Overfitting And Noise In Data." https://www.meegle.com/en_us/topics/overfitting/overfitting-and-noise-in-data
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