Machine Learning: Dimensionality Reduction via Linear Discriminant Analysis

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

Originally published on Towards AI.

A machine learning algorithm (such as classification, clustering or regression) uses a training dataset to determine weight factors that can be applied to unseen data for predictive purposes. Before implementing a machine learning algorithm, it is necessary to select only relevant features in the training dataset. The process of transforming a dataset in order to select only relevant features necessary for training is called dimensionality reduction. Dimensionality reduction is important because of three main reasons:

Prevents Overfitting: A high-dimensional dataset having too many features can sometimes lead to overfitting (model captures both real and random effects).Simplicity: An over-complex model having too… Read the full blog for free on Medium.

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Published via Towards AI