In machine learning, bias refers to a systematic error introduced by an algorithm that causes it to consistently make certain predictions. Bias can arise from assumptions made by the model to simplify the learning process.
- Glossary > Letter: B
What does "Bias" mean?

Use Cases
Model Building:
Understanding how bias affects the outcomes of data analysis.
Data Analysis:
Understanding how bias affects the outcomes of data analysis.

Importance
Fairness:
Ensuring models do not favor certain groups or outcomes unfairly.
Accuracy:
Improving the accuracy of predictions by addressing bias
Trustworthiness:
Building trust in AI systems by making them more reliable and unbiased.

Analogies
Bias in machine learning is like a crooked ruler. If you measure everything with a crooked ruler, all your measurements will be off in a consistent way. Similarly, a biased model consistently makes inaccurate predictions.
Ready to experience the full capabilities of the latest AI-driven solutions?
Contact us today to maximize your business’s potential!