teddyoweh/Dimensionality-Reduction-PCA
Dimensionality reduction is basically a process of reducing the amount of random features,attributes variables or in this case called dimensions in a dataset and leaving as much variation in the dataset as possible by obtaining a set of only relevant features to increase the effiency of a model.
This project helps data scientists and machine learning engineers prepare their datasets for model training. It takes a dataset with many attributes or variables and identifies only the most relevant ones. The outcome is a simplified dataset that can lead to faster training, more accurate predictions, and models that are less prone to errors.
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Use this if your machine learning models are training slowly, overfitting, or performing poorly due to too many irrelevant data points or features.
Not ideal if you need to retain every single piece of original data for interpretability or if your dataset already has a very small number of features.
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Apr 28, 2022
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