Overfitting vs Underfitting: A Simple Explanation

📰 Medium · Machine Learning

Learn to identify and address overfitting and underfitting in machine learning models to improve their performance and generalizability

intermediate Published 23 Apr 2026

Action Steps

Identify overfitting by checking if your model's training accuracy is significantly higher than its testing accuracy
Recognize underfitting when your model's training and testing accuracies are both low
Apply regularization techniques, such as L1 or L2 regularization, to reduce overfitting
Collect more data or use data augmentation to improve model generalizability and reduce underfitting
Use cross-validation to evaluate model performance and prevent overfitting

Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding overfitting and underfitting to build more robust models, while product managers can use this knowledge to inform model deployment strategies

Key Insight

💡 Overfitting occurs when a model is too complex and fits the training data too closely, while underfitting happens when a model is too simple and fails to capture the underlying patterns