Model Validation

What Is Model Validation? The Simple Definition

The process of determining whether a model fits its objectives or not. Model validation will typically focus on how well the model performs but can also consider model robustness, interpretability, and fairness.

The Technical Definition

Model validation is a systematic and rigorous process used in data science and machine learning to assess the accuracy, performance, and reliability of a predictive model.

It involves comparing the model’s predictions against real-world data to ensure that it produces reliable and trustworthy results.

Validation helps identify and quantify any potential errors, biases, or limitations in the model.

Explain It Like I’m Five

Model validation is like checking if a robot that predicts things is really good at its job. We give it some questions, and then we see if it gets the answers right by asking some real questions we already know the answers to.

Use It At The Water Cooler

How to use “model validation” in a sentence at work:

“Before deploying our new machine learning model for fraud detection, we need to complete the model validation process to make sure it accurately identifies fraudulent activities without too many mistakes.”

Related Terms

Machine Learning

Additional Resources

New York DFS AI Regulation, What Insurers Need To Know

New York DFS AI Regulation, What Insurers Need To Know

A must-read for insurance professionals. Instead of combing through pages and pages of legislation, this overview highlights everything you need to know. Understand fairness principles, and what is required for regulatory compliance for insurers licensed in New York. Stay informed about the best practices in AI governance to prevent discrimination and ensure transparency in insurance processes.

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