meowoodie/Regularized-RBM
A regularized version of RBM for unsupervised feature selection.
This helps data scientists and machine learning engineers pre-process high-dimensional datasets by identifying and filtering out irrelevant features before model training. It takes in raw, noisy data and outputs a refined dataset with only the most important variables, which can improve the performance and interpretability of subsequent analytical models.
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Use this if you need to simplify complex datasets by automatically selecting the most informative features, especially when dealing with text data or other high-dimensional inputs.
Not ideal if your dataset is already low-dimensional or if you require feature selection methods that offer direct human interpretability of selection criteria rather than an algorithmic approach.
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Language
Python
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Last pushed
Nov 20, 2019
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