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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.
Recognising underfitting helps you know when a model needs more complexity, more features, or more training time to be useful.
Trying to predict house prices using only the number of bedrooms would likely underfit — the model is too simple to account for location, size, condition, and other important factors.
Overfitting
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.
Feature
In machine learning, a feature is an individual measurable property or characteristic of the data being used to make predictions. Features are the input variables that a model uses to learn patterns and produce outputs.
Model
In AI, a model is the mathematical representation that a machine learning system builds from training data. It captures the patterns, relationships, and rules discovered during training and uses them to make predictions or generate outputs on new data.
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