Covariance Matrix Visualization Using Seaborn’s Heatmap Plot

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

Originally published on Towards AI.

This tutorial illustrates how the covariance matrix can be created and visualized using the seaborn library

Image by Benjamin O. Tayo

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. Feature selection and dimensionality reduction are 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 many features can be hard to interpret especially when features are correlated with each other.Computational Efficiency: A model trained on a… Read the full blog for free on Medium.

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Published via Towards AI