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Overfitting occurs when a machine learning model learns the training data too well — including its noise and outliers — and performs poorly on new, unseen data. The model essentially memorises the training examples rather than learning generalisable patterns.
An overfit model gives a false sense of accuracy during development but fails in production, making it critical to detect and prevent during model training.
A student who memorises every answer in a practice exam but cannot solve new questions on the real exam is analogous to an overfit model.
Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and new data. The model fails to learn enough from the training examples to make useful predictions.
Training Data
Training data is the collection of examples used to teach a machine learning model. The model analyses this data to discover patterns and relationships, which it then uses to make predictions or generate outputs on new, unseen data.
Supervised Learning
Supervised learning is a type of machine learning where the model is trained on labelled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict the correct output for new, unseen inputs.
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