What is Model Validation?
The process that helps us evaluate the performance of a trained model is called Model Validation. It helps us in validating the machine learning model performance on new or unseen data. It also helps us confirm that the model achieves its intended purpose.
What is Model Validation and Why is it Important?
Have you ever wondered if there is a way to check or evaluate the performance of a machine learning model you’ve trained? Is there a way or method to understand how the model responds to new or unseen data? The answer is yes, and it’s called Model Validation.
Before diving deeper into the article, let’s take a look at the article’s outline:
Table of Content
- What is Model Validation?
- Types of Model Validation
- 1. In-Sample Validation
- 2. Out-of-Sample Validation
- Importance of Model Validation
- Key Components of Model Validation
- 1. Data Validation
- 2. Conceptual Review
- 3. Testing
- Achieving Model Generalization
- Model Validation Techniques
- Benefits of Model Validation
- Conclusion
- Model Validation -FAQs