Understanding Bias, Variance, and Fairness in Machine Learning

Machine learning models are powerful tools for making predictions, but their effectiveness depends on how well they balance critical factors like bias, variance, fairness, and fit. These concepts are important in a model’s performance, reliability, and ethical implications.

In this lesson, we’ll discuss some challenges machine learning engineers face while designing a precise machine learning model.

Bias

Modeling is all about finding the best possible match to a real-world problem. This is often achieved by approximating the real-world problem using s simplified model based on some assumptions. However, since the model is just an approximation, there is always an error between the simplified representation and the real-world scenario.

A high bias occurs when we make overly simplistic assumptions about the data and use a model that doesn’t fit it. For example, we try to fit linear regression for non-linear data points. The illustration below depicts the bias in a regression model:

In simple terms, high bias causes the model to underfit, where the model fails to capture the important characteristics in the input data.

How to reduce bias in a model

There are several techniques that can be employed to reduce bias in a ...