Machine Learning: Dimensionality Reduction via Principal Component Analysis

Author(s): Benjamin Obi Tayo Ph.D.

Originally published on Towards AI.

How does PCA work?

In machine learning, a dataset containing features (predictors) and discrete class labels (for a classification problem such as logistic regression); or features and continuous outcomes (for a linear regression problem), is used to build a predictive model that can make predictions on unseen data. The predictive power of a model depends greatly on the quality and size of the training dataset.

Generally, the larger the dataset the better, however, there is always going to be a tradeoff between the size of the dataset and computational time needed for training. It turns out that in some very large datasets, there might be… Read the full blog for free on Medium.

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Published via Towards AI