📌 Overfitting Checks Summary
Overfitting checks are methods used to ensure that a machine learning model is not just memorising the training data but can also make accurate predictions on new, unseen data. Overfitting happens when a model learns too much detail or noise from the training set, which reduces its ability to generalise. By performing checks, developers can spot when a model is overfitting and take steps to improve its general performance.
🙋🏻♂️ Explain Overfitting Checks Simply
Imagine you are studying for a test and you only memorise the answers to practice questions, rather than understanding the main ideas. You might do well on the practice questions but struggle with new ones. Overfitting checks help make sure a model is not just memorising but actually learning, so it does well on all types of questions.
📅 How Can it be used?
Overfitting checks can be applied during model development to ensure the model performs well on both training and validation data.
🗺️ Real World Examples
A company developing a speech recognition system uses overfitting checks by testing the model on voice samples from people not included in the training data. This helps ensure that the system understands a variety of voices and accents, not just those it has heard before.
A hospital building a model to predict patient readmission uses overfitting checks by evaluating model performance on data from a different year than the training data. This ensures the model works reliably on new patient records.
✅ FAQ
What is overfitting in simple terms?
Overfitting happens when a machine learning model learns the training data too well, including the tiny details and noise that do not actually help it make predictions on new data. Think of it like memorising answers to a test rather than understanding the subject. As a result, the model might perform brilliantly on the training data but struggle when faced with anything new.
How can I check if my model is overfitting?
One of the easiest ways to check for overfitting is to compare your model’s performance on training data versus new, unseen data. If it does much better on the training set than on fresh data, it is likely overfitting. Using techniques like cross-validation or keeping a separate test set can help you spot these differences.
Why is it important to prevent overfitting?
Preventing overfitting is important because a model that only works well on the data it has already seen is not very useful. In real life, we want models to handle new situations and make good predictions on data they have never encountered before. By checking for overfitting, we make sure our models are genuinely learning and not just memorising.
📚 Categories
🔗 External Reference Links
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
Logistics Optimization
Logistics optimisation is the process of improving how goods, materials, or information move from one place to another. It aims to reduce costs, save time, and make sure deliveries happen as efficiently as possible. This often involves planning routes, managing inventory, and coordinating transport methods. Companies use logistics optimisation to make better decisions about shipping, storage, and distribution. By using data and technology, they can spot inefficiencies and adjust their operations to meet customer demand more effectively.
Stakeholder Engagement Plan
A Stakeholder Engagement Plan is a document that outlines how a project or organisation will communicate and interact with people or groups affected by its work. It identifies who the stakeholders are, what their interests or concerns may be, and the best ways to involve them in the process. The plan also sets out methods for gathering feedback, addressing issues, and keeping stakeholders informed throughout the project's life.
Semantic Inference Models
Semantic inference models are computer systems designed to understand the meaning behind words and sentences. They analyse text to determine relationships, draw conclusions, or identify implied information that is not directly stated. These models rely on patterns in language and large datasets to interpret subtle or complex meanings, making them useful for tasks like question answering, text summarisation, or recommendation systems.
Neural Representation Analysis
Neural Representation Analysis is a method used to understand how information is processed and stored within the brain or artificial neural networks. It examines the patterns of activity across groups of neurons or network units when responding to different stimuli or performing tasks. By analysing these patterns, researchers can learn what kind of information is being represented and how it changes with learning or experience.
Token Binding
Token Binding is a security technology that helps to prevent certain types of attacks on web sessions. It works by linking a security token, such as a session cookie or authentication token, to a specific secure connection made by a user's browser. This means that even if someone tries to steal a token, it cannot be used on another device or connection, making it much harder for attackers to hijack sessions or impersonate users. Token Binding requires support from both the user's browser and the server hosting the website or service.