Parametric vs non-parametric models — what's the difference?
easyAnswer
- Parametric models have a fixed number of parameters that doesn't grow with the dataset (linear regression, logistic regression, GLMs, Naive Bayes).
- They make strong assumptions about the functional form and are fast + data-efficient when those assumptions hold.
- Non-parametric models grow in complexity with the data (KNN, decision trees, kernel methods, Gaussian processes).
- They are more flexible but need more data and are prone to overfit.
Check yourself — multiple choice
- Non-parametric means no parameters at all
- Parametric models have a fixed parameter count; non-parametric complexity grows with data
- KNN is a parametric model
- Linear regression is non-parametric
Parametric = fixed capacity. Non-parametric = capacity grows with data.
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