A/B testing, a powerful tool used by many industries, compares two or more versions a model, an algorithm or a system to see which one performs better under real-world conditions. A/B tests are a powerful tool for data scientists, product teams, and machine learning experts to make evidence-based decisions. Data Science Course in Pune
A/B tests are a great way to compare two models in a clear and empirical manner. When a company wants to test out a new recommendation engine, it can use A/B testing to divide their users into two groups. Group A will continue to receive recommendations from the existing model while Group B will see results from the new model. Teams can measure key performance indicators (KPIs), such as click-through rates, conversion rates, or revenue per sessions, to determine which model performs best in practice, not just theoretically or based on historical data.
