AI GlossaryUnderfittingENTRY — Machine Learning
[Machine Learning]
Underfitting.
When a model is too simple to capture the underlying patterns in the data.
In-depth explanation
01Underfitting occurs when a model lacks the complexity to learn the relationship between inputs and outputs. Signs include poor performance on both training and validation data. Solutions include using more complex models, adding features, reducing regularization, or training longer.
Examples
02EX. 01
Using linear regression for highly non-linear data
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03Ensemble LearningCombining multiple models to produce better predictions than any single model.04FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.05Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.06Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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