Outlier Detection and Treatment: A Beginner's Guide

Author(s): Swetha Lakshmanan

Originally published on Towards AI.

Nature of Outliers:

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One of the most important steps in data pre-processing is outlier detection and treatment. Machine learning algorithms are very sensitive to the range and distribution of data points. Data outliers can deceive the training process resulting in longer training times and less accurate models. Outliers are defined as samples that are significantly different from the remaining data. Those are points that lie outside the overall pattern of the distribution. Statistical measures such as mean, variance, and correlation are very susceptible to outliers.

A simple example of an outlier is here, a point that deviates from the overall pattern.

Outliers can occur… Read the full blog for free on Medium.

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Published via Towards AI