Home Artificial Intelligence Dimensionality Reduction with Scikit-Learn: PCA Theory and Implementation

Dimensionality Reduction with Scikit-Learn: PCA Theory and Implementation

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Dimensionality Reduction with Scikit-Learn: PCA Theory and Implementation

The Curse of Dimensionality could be tamed! Learn how one can do it with Python and Scikit-Learn.

Towards Data Science
Image source: unsplash.com.

Within the novel Flatland, characters living in a two-dimensional world find themselves perplexed and unable to understand after they encounter a three-dimensional being. I exploit this analogy as an instance how similar phenomena occur in Machine Learning when coping with problems involving 1000’s and even tens of millions of dimensions (i.e. features): surprising phenomena occur, which have disastrous implications on our Machine Learning models.

I’m sure you felt stunned, no less than once, by the huge variety of features involved in modern Machine Learning problems. Every Data Science practitioner, ultimately, will face this challenge. This text will explore the theoretical foundations and the Python implementation of essentially the most used Dimensionality Reduction algorithm: Principal Component Evaluation (PCA).

Why do we want to scale back the variety of features?

Datasets involving 1000’s and even tens of millions of features are common nowadays. Adding recent features to a dataset can usher in worthwhile information, nonetheless, they may slow the training process and make it harder to search out good patterns and solutions. In Data Science this known as the Curse of Dimensionality and it often results in skewed interpretation of information and inaccurate predictions.

Machine learning practitioners like us can profit from the indisputable fact that for many ML problems, the variety of features could be reduced consistently. For instance, consider an image: the pixels near the border often don’t carry any worthwhile information. Nevertheless, the techniques to soundly reduce the variety of features in a ML problem usually are not trivial and want a proof that I’ll provide on this post.

Image by the creator.

The tools I’ll present not only simplify the computation effort and boost the prediction accuracy, but they may also function a tool to graphically visualize high-dimensional data. Because of this, they’re essential to speak your insights…

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