bias

A bias is a systematic deviation from truth or fairness that can arise in data, models, or decision processes. In statistics, bias typically means the difference between an estimator’s expected value and the true value of the parameter it’s intended to estimate.

In machine learning, algorithmic bias refers to systematic patterns in model behavior that produce unfair or discriminatory outcomes across groups. It often stems from imbalanced data, labels, or design choices, as well as from systemic inequities baked into the institutions that produced the data and from the cognitive biases of the people who build and use the system.

Practitioners assess such bias with group-fairness metrics like demographic parity and equalized odds. They mitigate biases using better data collection, sample reweighting, fairness-constrained training objectives, and post-processing adjustments to model outputs.

Related Resources

Tutorial

Split Your Dataset With scikit-learn's train_test_split()

In this tutorial, you'll learn why splitting your dataset in supervised machine learning is important and how to do it with train_test_split() from scikit-learn.

intermediate data-science machine-learning numpy

For additional information on related topics, take a look at the following resources:

• Splitting Datasets With scikit-learn and train_test_split() (Course)
• Python AI: How to Build a Neural Network & Make Predictions (Tutorial)
• Linear Regression in Python (Tutorial)
• Logistic Regression in Python (Tutorial)
• Python Statistics Fundamentals: How to Describe Your Data (Tutorial)
• Split Your Dataset With scikit-learn's train_test_split() (Quiz)
• Building a Neural Network & Making Predictions With Python AI (Course)
• Python AI: How to Build a Neural Network & Make Predictions (Quiz)
• Starting With Linear Regression in Python (Course)
• Linear Regression in Python (Quiz)
• Python Statistics Fundamentals: How to Describe Your Data (Quiz)