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What is Principal Component Analysis (PCA) in Data Science? |
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Answer» Response: Principal Component Analysis (PCA) is a dimensionality reduction technique used in Machine learning. That means, it is an approach to extract or detect KEY features (in the form of components) from the input dataset which MAY have a LARGE set of features. Hence it is a kind of a feature selection method. The objective is to select a few features or variables that represent as much information as can be possible for us to be able to use those for the learning process. Hence it is used to overcome redundancy in features in the dataset and by identifying those, a decision can be taken to OPTIMIZE them or drop them. This method is generally applied on DATASETS with numbers. |
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