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1 month 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
Linear Discriminant Analysis (LDA) is a dimensionality reduction and classification technique used to find a linear combination of features that best separates two or more classes of objects. By projecting data onto a lower-dimensional space, LDA maximizes the separation between classes while minimizing the variance within each class. This technique is particularly useful for pattern recognition and classification tasks where the data is linearly separable.
Use Case
Adding LDA to the project would enhance its ability to handle classification problems by reducing the dimensionality of the data while preserving the class separability. This feature would be beneficial in scenarios with high-dimensional datasets, such as facial recognition or medical diagnosis, where LDA helps improve classification accuracy and computational efficiency.
Benefits
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Priority
High
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