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parametric classifiers
A parametric classifier is a machine learning and statistical pattern recognition model that assumes the underlying data follows a specific mathematical distribution or functional form defined by a fixed set of parameters. Rather than relying on individual training instances during prediction, the classifier estimates a finite number of parameters, such as class-conditional means and covariance matrices, to establish the decision boundaries separating different classes. Because the complexity of the model is predetermined and the number of parameters does not grow with the size of the training dataset, parametric classifiers are computationally efficient, straightforward to train, and require less memory than non-parametric alternatives. Common examples include linear discriminant analysis, quadratic discriminant analysis, and Gaussian naive Bayes, all of which perform well when the chosen distributional assumptions align with the true structure of the data.
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