What is Overfitting?
When an AI model memorises its training data too closely and performs poorly on new, unseen data.
Why It Matters
Overfitting means your model looks great in testing but fails in the real world with actual users.
Real-World Example
A model that perfectly predicts training data outcomes but makes poor predictions on new customer data.
“Understanding terms like Overfitting matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Underfitting
When an AI model is too simple to capture the patterns in the data and performs poorly on everything.
Training Data
The dataset used to teach an AI model patterns and knowledge during its initial training.
Model Evaluation
The process of measuring how well an AI model performs on tasks it was designed for.
Hyperparameters
Settings you choose before training an AI model that control how the training process works.
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Related Terms
Training Data
The dataset used to teach an AI model patterns and knowledge during its initial training.
Model Evaluation
The process of measuring how well an AI model performs on tasks it was designed for.
Underfitting
When an AI model is too simple to capture the patterns in the data and performs poorly on everything.
Hyperparameters
Settings you choose before training an AI model that control how the training process works.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...