Explain the difference between parametric and non-parametric models.
- The ability to update parameters during training
- The flexibility in form
- The number of features used
- The use of hyperparameters
Parametric models assume a specific form for the function they're approximating, such as a linear relationship, and have a fixed number of parameters. Non-parametric models make fewer assumptions about the function's form, often resulting in more flexibility but also requiring more data.
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