Principal Component Analysis for Dimensionality Reduction
Explore how Principal Component Analysis transforms high-dimensional data into fewer features while preserving essential information. Understand the steps of standardization, covariance matrix calculation, and eigen decomposition. Learn to select key components for simplifying data and enhancing model efficiency. Practice implementation using Scikit-Learn on the Iris dataset.
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Principal Component Analysis
PCA stands for Principal Component Analysis. It helps us transform high-dimensional datasets (having a large number of features) into a low-dimensional one (having a smaller number of features) without losing too much information. These datasets can include images or simple structured datasets. This helps us deal with the curse of dimensionality, which results in complex models and difficulty in visualizing.
Data represented in lower dimensions can be easily visualized. It would also help in modelling as the model won’t have to take into account extraneous features represented in higher dimensions. It also helps us remove the Multi-collinearity situation in which some input features are correlated with each other and provide redundant information.
How PCA works?
- PCA begins by standardization each column at hand. This helps to standardize the range of continuous variables. If we don’t perform the standardization, the columns with a large range of values will dominate over those having small range of values in the calculations involved in the next steps. The following steps demonstrate standardization is done as follows.
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