![]() So how do we find the principal components? And PCA is essentially a projection of the dataset onto the principal components. ![]() These directions are called principal components. So it's important to find the directions of maximum variance in the dataset. The motivation behind the algorithm is that there are certain features that capture a large percentage of variance in the original dataset. Meaning it reduces the dimensionality of the feature space. How Does Principal Component Analysis (PCA) Work?īefore we go ahead and implement principal component analysis (PCA) in scikit-learn, it’s helpful to understand how PCA works.Īs mentioned, principal component analysis is a dimensionality reduction algorithm.
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