Definition
Overfitting refers to a situation where a model matches (memorizes) the training data set in such detail that the model fails to make correct predictions on new data. An overfit model is just like an invention that performs well in the lab but is unreliable in the real-world applications [1].
A good example of an overfitting model is just like a student preparing for WAEC, who memorizes past question papers word for word instead of understanding the underlying concepts. This student performs excellently on practice tests using previous years' exact questions (training data) but struggles when WAEC introduces new question formats or topics in the actual examination (testing data).
Origin
The term "overfitting" had its roots in the field of statistics and has been comprehensively studied in the context of regression analysis and pattern recognition. However, with the rise of artificial intelligence and machine learning, overfitting has risen in importance as a result of its critical implications on the performance of AI models. The concept has advanced significantly, with researchers continuously striving to develop techniques to reduce its negative impacts on model accuracy and generalization [3].
Context and Usage
Overfitting is a
critical consideration in applications where model accuracy on unseen data is
paramount.
- Search Engines: Overfitting in ranking algorithms can degrade user experience by prioritizing irrelevant results.
- Speech Recognition: It is very important to make sure models can generalize to varied accents and speech styles.
- Medical Diagnostics: Overfitting can result to wrong diagnoses of patients by capturing spurious correlations in training data.
- Fraud Detection: A model may flag legitimate transactions as fraudulent if it overfits patterns unique to training examples [4].
Detecting and avoiding overfitting through techniques like regularization and cross-validation is thus essential for creating models capable of robust performance on real data.
Why it Matters
Overfitting renders
the goal of a machine learning model useless. Addressing overfitting is very
important as a model's primary goal is to make accurate predictions on new,
unseen data, not just to replicate the training data. Generalization of a model
to new data is ultimately what allows us to use machine learning algorithms
every day to make predictions and classify data.
In Practice
A real-life case study of a company offering services related to overfitting can be seen in the case of C3 AI. C3 AI provides a rich machine learning development environment known as C3 AI ML Studio as part of the C3 AI Platform to enable data scientists to develop, train, test, deploy, and operate ML models at scale. Functions like “experiment and “model management” make it easy to avoid or correct for overfitting during each phase of the development process and to continually monitor the performance of deployed models to maintain and maximize accuracy over time [5].
See Also
Related Model
Training and Evaluation concepts:
- Parameters: Adjustable values in a model that are learned during training to make predictions
- Presence Penalty: Parameter that reduces repetition by penalizing tokens that have already appeared
- Pre-Training: Initial training phase where models learn general patterns from large datasets
- Prompt: Input text or instruction given to an AI model to generate a response
- Prompt Engineering: Craft of designing effective prompts to get desired AI responses
References
- GoogleforDevelopers. (n.d). Overfitting.
- Great Learning Editorial Team. (2025). Overfitting and Underfitting in Machine Learning.
- Lark Editorial Team. (2023). Overfitting.
- Lyzr Team. (2024). Overfitting.
- c3.ai. (2025). Overfitting.