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2 months ago Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!
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Feature Description
Gaussian Discriminant Analysis (GDA) is a classification algorithm that assumes class-conditional densities follow Gaussian distributions. It estimates parameters such as mean and covariance matrix for each class from training data, allowing it to compute posterior probabilities of class membership for new inputs using Bayes' theorem. GDA is efficient with small datasets, interpretable due to its probabilistic nature, and applicable in scenarios where Gaussian distributions reasonably describe class distributions, such as in facial recognition, medical diagnostics, and text categorization tasks. However, its performance relies on the accuracy of the Gaussian assumption and the availability of sufficient training data for reliable covariance estimation.
Use Case
-Medical Diagnostics -Financial Fraud Detection
Priority
High
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