What is Overfitting & Underfitting in Machine Learning?

Overfitting and underfitting in machine learning describe how well a model captures patterns from data. Overfitting occurs when a model learns noise and performs poorly on new data. Underfitting happens when the model fails to learn the data’s structure and performs poorly everywhere. Both are critical issues that affect model accuracy, reliability, and generalisation in real-world AI systems.

In this guide, you’ll read more about model fit, bias-variance trade-off, how to detect overfitting and underfitting, key differences between the two, and proven methods to fix them.

Let’s start by understanding Overfitting first.

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What is Overfitting in Machine Learning?

Overfitting in machine learning happens when a model performs extremely well on training data but fails to give accurate results on new or unseen data. It learns the patterns, but also the noise and random fluctuations within the training set. This makes the model too specialized to the data it has already seen.

In simple terms, the model becomes “too smart” for its own good, it memorizes rather than learns. As a result, it struggles to generalize when faced with real-world data.

Example:
Imagine training a deep neural network on just 500 images of cats and dogs. If the model starts memorizing the background, lighting, or position of the animals instead of recognizing the actual features, it will perform well on those same 500 images but fail on new ones.

Simple illustration of performance:

Dataset	Training Accuracy	Test Accuracy	Result
Model A (overfitted)	99%	68%	Overfitting
Model B (balanced)	92%	90%	Good fit
Model C (underfitted)	70%	68%	Underfitting

How to identify overfitting early:

• Compare performance on training and validation datasets.
• Plot learning curves to see if the validation error increases after a point.
• Use cross-validation to check model stability across data splits.

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What is Underfitting in Machine Learning?

Underfitting in machine learning occurs when a model is too simple to capture the underlying structure or relationships in the data. It performs poorly on both training and test datasets because it hasn’t learned enough from the input features. This usually happens when the model cannot find meaningful patterns, leading to low accuracy and high errors everywhere.

When we talk about overfitting and underfitting in machine learning, underfitting represents the high-bias side of the issue. 

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Common signs of underfitting:

• High training and validation errors.
• Model predictions are overly simplistic.
• Adding more training data doesn’t improve results.
• Model struggles to capture trends in both train and test data.

Typical causes of underfitting in machine learning:

• Model is too simple (for example, using Linear Regression for non-linear data).
• Not enough features or important variables ignored.
• Excessive regularization restricting model learning.
• Too few training iterations or early stopping.

Example:
Imagine using a straight line (Linear Regression) to fit data that follows a curved trend. The line won’t capture the pattern, this is underfitting. The model’s simplicity prevents it from learning complex relationships.

Performance comparison:

Model Type	Training Accuracy	Test Accuracy	Result
Model A	70%	68%	Underfitting
Model B	92%	89%	Good fit
Model C	99%	68%	Overfitting

How to fix underfitting:

• Use a more complex model with additional parameters.
• Add relevant features or create new ones through feature engineering.
• Reduce regularization strength.
• Train the model longer or tune hyperparameters for better learning.

In short, underfitting in machine learning means the model hasn’t learned enough to represent the data properly. 

Also Read: Exploring the Scope of Machine Learning: Trends, Applications, and Future Opportunities

Difference Between Overfitting and Underfitting in Machine Learning

The difference between overfitting and underfitting in machine learning lies in how a model learns from data and how well it performs on unseen samples. Both problems affect model accuracy but in opposite ways. 

In simple terms:

• Overfitting = Model is too complex and memorizes the data.
• Underfitting = Model is too simple and fails to capture relationships.

Key differences:

Aspect	Overfitting	Underfitting
Definition	Learns training data too well, including noise	Fails to learn from training data
Cause	Model is too complex	Model is too simple
Bias	Low bias	High bias
Variance	High variance	Low variance
Training Accuracy	Very high	Low
Test Accuracy	Low	Low
Example	Deep neural network trained on small dataset	Linear model used for nonlinear data
Result	Performs well on training data but poorly on new data	Performs poorly on both training and new data

How they affect model performance:

• Overfitting makes a model unreliable when exposed to real-world data because it has learned irrelevant details.
• Underfitting makes the model inaccurate because it hasn’t captured enough of the data’s structure.

Also Read: 5 Breakthrough Applications of Machine Learning

How to balance the two:

• Use techniques like regularisation, dropout, and cross-validation to control overfitting.
• Increase model complexity, add features, or reduce regularisation to fix underfitting.
• Continuously monitor training and validation accuracy to ensure the model generalises well.

In short, the difference between overfitting and underfitting in machine learning comes down to balance. A good model sits in the middle, complex enough to learn patterns but simple enough to generalise effectively.

How to Detect Overfitting and Underfitting

Detecting overfitting and underfitting in machine learning helps you understand how well your model is learning and generalizing. You can identify both by analyzing model performance on training and validation datasets and by using evaluation metrics.

1. Compare training and validation performance

• Overfitting: Training accuracy is high, but validation accuracy is much lower.
• Underfitting: Both training and validation accuracy are low.
• The gap between training and validation scores reveals if the model is too complex or too simple.

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Example:

Model Type	Training Accuracy	Validation Accuracy	Observation
Model A	99%	68%	Overfitting
Model B	70%	68%	Underfitting
Model C	91%	89%	Good fit

2. Use learning curves
Learning curves show how model performance changes with training progress.

• In overfitting, the training curve keeps improving, while the validation curve stops or worsens.
• In underfitting, both curves stay flat and high in error, showing the model isn’t learning enough.

3. Check performance metrics
For classification:

• Accuracy, F1-score, Precision, Recall
  For regression:
• RMSE, MAE, and R² score

If metrics on the training set are much better than on the validation or test set, overfitting is likely.

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4. Use cross-validation
Cross-validation tests your model on multiple data splits.

• Large variations in performance across folds indicate overfitting.
• Consistently low performance across all folds indicates underfitting.

5. Analyze prediction behavior

• Overfitted models often make confident but wrong predictions on new data.
• Underfitted models give predictions close to the mean or random outputs, showing they haven’t captured the pattern.

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6. Visualize errors
Plot actual vs. predicted values or residuals.

• Overfitting: Errors are small on training data but large on test data.
• Underfitting: Errors are large everywhere.

By regularly monitoring these patterns, you can detect overfitting and underfitting in machine learning early. This helps fine-tune model complexity, training strategy, and data preprocessing for better generalization.

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Strategies to Avoid Overfitting and Underfitting

Avoiding overfitting and underfitting in machine learning is all about finding the right balance between model complexity, data quality, and training strategy. The goal is to build a model that performs well not just on training data but also on unseen data.

1. Strategies to Avoid Overfitting

To prevent Overfitting:

a. Simplify the model
Use fewer parameters or layers if the model is too complex. A simpler architecture often generalizes better.

b. Use regularization techniques
Add penalties to large weights to prevent the model from becoming overly flexible.

• L1 Regularization (Lasso): Shrinks less important features to zero.
• L2 Regularization (Ridge): Reduces weight magnitude evenly.

Also Read: Different Types of Regression Models You Need to Know

Table: Regularization Overview

Technique	Description	Common Use
L1 (Lasso)	Removes unnecessary features	Linear/Logistic Regression
L2 (Ridge)	Prevents over-reliance on any single feature	Regression, SVM
Dropout	Randomly disables neurons during training	Neural Networks
Early Stopping	Stops training when validation loss increases	Deep Learning

c. Collect more data
A larger dataset gives the model more diverse examples, helping it generalize better.

d. Apply data augmentation
In image or text models, augmenting data (rotations, translations, paraphrasing) helps prevent memorization of specific examples.

e. Use cross-validation
Validate performance on multiple subsets of data to detect overfitting early.

f. Apply dropout and early stopping
Dropout prevents co-adaptation of neurons. Early stopping halts training when validation performance stops improving.

g. Use ensemble methods
Techniques like Random Forest, Bagging, and Boosting combine multiple models to stabilize performance and reduce overfitting.

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2. Strategies to Avoid Underfitting

Underfitting happens when the model is too simple to capture the data’s complexity. To fix it:

a. Increase model complexity
Use a more powerful model (e.g., Random Forest instead of Linear Regression) that can capture nonlinear relationships.

b. Add or engineer better features
Include variables that add predictive power. For example, combine or transform features to reveal hidden patterns.

c. Reduce regularization
Excessive regularization can oversimplify the model. Tune regularization strength to allow more flexibility.

d. Train for more epochs
Extend training duration to give the model more time to learn complex patterns.

e. Tune hyperparameters
Adjust learning rate, depth, and other model-specific parameters for better performance.

f. Clean and preprocess data properly
Ensure data is scaled, encoded, and free from missing or irrelevant information to help the model learn effectively.

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3. Balancing Both Issues

• Start with a simple model and gradually increase complexity.
• Monitor both training and validation performance during training.
• Use validation curves and cross-validation to detect imbalance.
• Combine techniques like regularization and feature engineering to reach optimal performance.

When managed carefully, these strategies prevent both extremes of overfitting and underfitting in machine learning, leading to models that are both accurate and reliable across real-world data.

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Common Use-Cases and Real-World Scenarios

Overfitting and underfitting in machine learning affect almost every domain where models learn from data. Recognizing these issues in real-world contexts helps you design models that generalize better and perform reliably in production.

1. Image Classification

Overfitting example:
A deep convolutional neural network trained on a small image dataset might memorize the background or lighting of training images. It performs well on known images but fails to recognize new ones.

Underfitting example:
Using a simple logistic regression model for complex image classification tasks like facial recognition. It cannot capture intricate pixel patterns, leading to low accuracy.

Fix:

• Add more training images or apply data augmentation.
• Use dropout, batch normalization, and regularization to reduce overfitting.

Also Read: Classification Model Using Artificial Neural Networks (ANN) with Keras

2. Sentiment Analysis

Overfitting example:
A text classification model trained on limited social media data learns specific phrases instead of understanding sentiment. It misclassifies new posts with slightly different wording.

Underfitting example:
A model that uses only word counts (Bag of Words) might miss context or tone, causing poor accuracy.

Fix:

• Use pre-trained embeddings like Word2Vec or BERT to capture context.
• Apply regularization and evaluate on diverse datasets.

Also Read: Twitter Sentiment Analysis in Python: 6-Step Complete Guide [2025]

3. Stock Price Prediction

Overfitting example:
A model trained on short-term stock data might capture temporary fluctuations instead of long-term patterns. It performs well historically but fails in live trading.

Underfitting example:
A linear regression model that ignores external factors like news or macroeconomic trends cannot capture true market behavior.

Fix:

• Use feature engineering to include time-based, technical, and sentiment features.
• Apply cross-validation to ensure consistent predictions.

Also Read: Build a Stock Price Prediction Model Using ML Techniques

4. Fraud Detection

Overfitting example:
A fraud detection model memorizes patterns from known fraud cases but misses new, evolving ones.

Underfitting example:
A model too simple to detect subtle transactional anomalies fails to identify potential fraud.

Fix:

• Use ensemble models (e.g., XGBoost) to capture complex relationships.
• Retrain frequently to adapt to new fraud trends.

Also Read: Fraud Detection in Transactions with Python: A Machine Learning Project

5. Housing Price Prediction

Overfitting example:
A model learns every detail from the training data, such as rare local events, and fails when predicting prices for other regions.

Underfitting example:
A model that ignores location and amenities gives nearly identical predictions for all houses.

Fix:

• Use relevant features like neighborhood score, proximity, and house age.
• Apply regularization and feature selection to avoid unnecessary noise.

Also Read: House Price Prediction Using Machine Learning in Python

In every case, overfitting and underfitting in machine learning can be detected and corrected through careful model design, robust validation, and consistent monitoring. The right combination of feature engineering, model tuning, and validation ensures the model learns patterns that truly generalize to real-world data.

Conclusion

Overfitting and underfitting in machine learning represent two extremes of model performance. Overfitting makes a model memorize data, while underfitting prevents it from learning enough. The key is balance, building a model that captures true patterns without noise. By applying techniques like regularization, cross-validation, and feature tuning, you can create models that generalize well and perform accurately on unseen data, ensuring reliable results across real-world applications and long-term learning efficiency.

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