Glossary of Terms

Inductive Bias

Definition of

Inductive Bias

Inductive Bias, also known as learning bias, is a set of assumptions used by a learner to predict outputs and given inputs that have not yet been encountered. In machine learning, it aims to construct algorithms with the ability to learn to predict certain target outputs.

Related Services

Related Industries

Stay in the Loop!

Subscribe to our newsletter and get the latest updates, exclusive content, and insights on Data Ops, Machine Learning, and emerging tech startups.

Related Content

Our Managed Services Model

Our Managed Services Model

Learn how our managed services are designed to shoulder all operational responsibilities, offering clients streamlined, process-based operations under a flat monthly fee, allowing them to focus on growth.

How BUNCH Became a 24/7 Operations Powerhouse

How BUNCH Became a 24/7 Operations Powerhouse

Our 24/7 outsourcing services ensure seamless, efficient operations for businesses worldwide. From shift scheduling to cultural sensitivity, we guarantee continuous support in all time zones.

Ethical Supply Chain: Your Reputation Extends to Your Outsourced Teams

Ethical Supply Chain: Your Reputation Extends to Your Outsourced Teams

Explore the importance of ethical supply chain management in outsourcing. Learn how BUNCH ensures fair wages, strict working conditions, comprehensive mental health support, and end-to-end compliance to maintain integrity and enhance your brand's reputation.

Scaling Data Labeling Teams Without Compromising on Quality

Scaling Data Labeling Teams Without Compromising on Quality

Discover how companies can scale data labeling for ML models without sacrificing quality. Learn about double-pass annotation, AI integration, dedicated teams, continuous training, and robust project management to maintain precision and efficiency.

How We Are Obsessed About Data Quality and Why

How We Are Obsessed About Data Quality and Why

We understand the importance of reliable data quality for training datasets and precision in moderating user-generated content. Learn how we apply rigorous QA in all our processes.