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How Do You Validate a Model? All You Need to Know About Model Validation

Updated: 6 days ago

Within the dynamic sphere of Machine Learning, building robust models is pivotal for accurate predictions, actionable insights, better business decisions, and reduced risks. A critical role in this pursuit is model validation—a process instrumental in fortifying machine learning models to seamlessly adapt to novel data landscapes.


The model validation process can be performed through various methods and approaches. While certain techniques involve tradeoffs, there are occasions where superior validation methods emerge, providing more robust assessments for your models.


Validation is not only a key factor for developing strong models, but it also aids in understanding the model’s potential impact and associated business risks. Despite its importance in a model’s lifecycle, validation does not get enough attention these days. Therefore, we are raising awareness for the unsung hero of the model development process. In this blog post, we will go over what model validation is, why it is important, and how it is achieved.


Spongebob rainbow meme with Model Validation written in meme font

What Is Model Validation?

During model development, a model may perform well on the training data. Does that mean you can trust it in the real world? No! Before you even think about deployment, you need to validate your model. 


Model validation refers to the rigorous assessment of a machine learning model's performance and generalizability. It's not merely about getting the model to work—it's about ensuring that it performs reliably on unseen data, which is the ultimate test of its usefulness.

ML models, much like laboratory experiments, harbor uncertainty regarding their success when applied in real-world scenarios.

The validation process entails testing the model on new datasets, known as the Validation Set, to assess how well the model works on unseen data. Validation is a comprehensive process that covers all aspects of performance, fairness, robustness, vulnerabilities, and explainability. Today, most only validate performance and fairness, but the process is more dynamic than that. 



ML models, much like laboratory experiments, harbor uncertainty regarding their success when applied in real-world scenarios. The development environment is like a safe bubble compared to the production environment, where there are risks such as malicious attacks, data and concept drifts, or runtime framework issues. Model validation ensures that potential risks in models are lowered to a level that's considered acceptable before deploying them. 


Why Is Model Validation Important?

Now that we've gained insight into Model Validation, we can truly appreciate its vital role in the entire model development process. While some might consider validation a tedious task, it is a crucial step for the following reasons:  


Three graphs drawn by Andrew Ng showing high bias, just right, and overfitting
Source: Andrew Ng’s Machine Learning Coursera class

  • Generalization: As the model steps into the real world and faces unfamiliar data, its ability to maintain peak performance becomes crucial. This underscores the significance of validation, as it assesses the model's capacity to excel with unseen data that differs from its initial development/training phase.

  • Trustworthiness: Validated models are more trustworthy because the outcomes are easier to explain, there are fewer vulnerabilities, and they perform better. In applications where the consequences of incorrect predictions are significant, like in healthcare or finance, it's crucial to have confidence in the model's accuracy. Model validation ensures reliability.

  • Mitigating Overfitting: Data scientists often deal with complex models and rich data. Overfitting, where a model performs exceptionally well on training data but poorly on unseen data, is a common pitfall. Model validation serves as our guardian against this problem, helping us detect and prevent overfitting.

  • Ethical Considerations: Machine learning models can inadvertently perpetuate biases and discrimination if not validated properly. Model validation is a means of ensuring fairness and preventing unjust outcomes, addressing important ethical concerns in AI and data science.

  • Regulatory Compliance: In many high-risk industries, especially those involving sensitive data, there are regulatory requirements for model validation. Compliance with these regulations is not only good practice but often a legal obligation.


By conducting an unbiased evaluation of the model’s performance, we empower it to make accurate predictions in the real world. It is also an essential part of model risk management because validation ensures the model doesn’t create more problems than it solves. This process offers additional advantages, including: 


  • Lowers costs by helping to avoid unnecessary expenses, optimize resource usage, and mitigate risks associated with model deployment.

  • Easily discover errors by systematically evaluating and comparing your model's predictions against actual data.


How Do You Validate a Model?

The ultimate goal of any machine learning model is to assimilate knowledge from existing data and extend its capacity to make accurate predictions on entirely novel instances. Consequently, the choice of the most suitable machine learning algorithm and training data for a given dataset is of paramount importance.


Each model has unique strengths and weaknesses - some excel with small datasets, while others thrive with vast data. Therefore, when multiple models are deployed on similar datasets, they may yield distinct predictions with varying degrees of accuracy. ​​This underscores the compelling need for a robust and comprehensive model validation process.


To achieve this, data scientists have an array of model validation techniques at their disposal. These techniques encompass conventional approaches, such as train/test splitting, K-fold cross-validation, and leave-one-out cross-validation, as well as more advanced methods, including bootstrapping and ensemble techniques. 


Diagram showing cross validation
Example Illustration of Cross Validation

The selection of the most appropriate model validation technique hinges on factors like the dataset's size and the level of desired accuracy. However, it's important to note that, while these methods are invaluable tools in a data scientist's arsenal, they may not always suffice to guarantee model reliability and performance. While these techniques address generalization, performance, and overfitting, they do not tackle fairness, vulnerabilities, or transparency. 


How Does Citrusˣ Help With Model Validation?

The Citrusˣ solution provides continuous actionable validation that goes beyond the common techniques we discussed previously. We provide various performance and proprietary robustness metrics to identify problems on a local, global, and cohort level, and our explainability capabilities allow you to understand and interpret how your model is working on a broader level. 


Two graphs from the citrusx UI showing the accuracy and robustness metrics
Citrusx Model Validaton Measurements

There are several features we use to help you validate your model. These include Accuracy measurements, Stability, Complexity, Data Coverage (Sparsity), Certainty, and Feature Bias (Vulnerabilities). 


Our unique technique pinpoints and identifies specific problematic places rather than using the average score for the entire data space. Additionally, the Complexity score allows you to understand the extent to which the prediction function can be simplified. 


Final Thoughts

Model validation is the unsung hero on the journey to creating robust, trustworthy machine learning models. At Citrusˣ, our validation techniques are not merely the first step but an enduring commitment to model integrity. Beyond the initial validation, our proprietary methods delve deeper, comprehensively assessing a model's influence and potential limitations. This multifaceted examination guarantees its efficacy while pinpointing opportunities for improvement.


Citrusˣ’s ongoing validation solution empowers superior decision-making. By establishing a continuous feedback loop, we facilitate persistent model refinement and updates, ensuring models remain robust, fair, and explainable.



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