The Importance of Model ValidationValidating your machine learning model outcomes is all about making sure you’re getting the right data and that the data is accurate. Validation catches problems before they become big problems and is a critical step in the implementation of any machine learning model.
SecurityOne of the most critical aspects of model validation is looking for security vulnerabilities. Training data and machine learning model data are all valuable, especially if that data is private or sensitive. It’s possible for machine learning models to accidentally leak its data, meaning your validation techniques should check for data leak vulnerability. It’s also important to take serious security measures before entering your training data into the machine learning model. For example, you can anonymize or pseudonymize your data.
ReliabilityValidating your machine learning model is also important for checking the reliability of your model. You want to understand your model and get to know its strengths and weaknesses. Knowing your model well will help you to interpret and look for errors in its output later on. Knowing how your model behaves will also help you to take note of any drift or biases that may occur.
Avoid BiasWhile machine learning technology has revolutionized the computing space, it’s only as good as its creators. That means many machine learning models come with bias built in. Your algorithm may be biased and/or your training data may also be biased. Knowing how to look for bias and how to fix the bias in your machine learning model is an important aspect of model validation and making the world of machine learning a better, more equitable place.
Prevent Concept DriftConcept drift is the situation where a machine learning model has been allowed to degrade and what it predicts varies from what it is intended to predict. Concept drift happens, but how the model drifts is unpredictable. Drift is harmful to the machine learning model as the output data becomes less useful. While initial machine learning model validation won’t catch concept drift, proper maintenance and regular testing will. Concept drift happens over time, but it’s completely preventable with routine maintenance.
The Right Data and The Right PeopleIf you’re building a machine learning model or are interested in adding AI technology to your company, it’s important to know that the right training data and the right people to validate and maintain that model are critical. Without validating your model or continuous maintenance, your machine learning model can become obsolete.
Continuous MonitoringNo machine learning model is perfect — nor do they ever stay perfect. A machine learning model takes continuous monitoring and adjustments to make sure that it continues to put out accurate, relevant information. While machine learning is mostly autonomous once it’s trained, validation and monitoring require human-in-the-loop operations. It’s important for your machine learning model to be regularly maintained and checked by a human. This can be done on a regular schedule or in real-time.
Model Validation TechniquesThere are a number of different model validation techniques, choosing the right one will depend upon your data and what you’re trying to achieve with your machine learning model. These are the most common model validation techniques.
Train and Test Split or HoldoutThe most basic type of validation technique is a train and test split. The point of a validation technique is to see how your machine learning model reacts to data it’s never seen before. All validation methods are based on the train and test split, but will have slight variations. With this basic validation method, you split your data into two groups: training data and testing data. You hold back your testing data and do not expose your machine learning model to it, until it’s time to test the model. Most people use a 70/30 split for their data, with 70% of the data used to train the model.
ResubstitionThe resubstition validation method is where you use all of your data as training data. Then, you compare the error rate of the machine learning model’s output to the actual value from the training data set. This is an easy to do method and it can help you quickly find the gaps in your data.
K-Fold Cross-ValidationA k-fold cross-validation is similar to the test split validation, except that you will split your data into more than two groups. In this validation method, “K” is used as a placeholder for the number of groups you’ll split your data into. For example, you can split your data into 10 groups. One group is left out of the training data. Then you validate your machine learning model using the group that was left out of the training data. Then, you cross validate. Each of the 9 groups used as training data are then also used to test the machine learning model. Each test and score can give you new information about what’s working and what’s not in your machine learning model.
Random SubsamplingRandom subsampling functions in the same way to validate your model as does the train and test validation model. The key difference is that you’ll take a random subsample of your data, which will then form your test set. All of your other data that wasn’t selected in that random subsample is the training data.
BootstrappingBootstrapping is a form of machine learning model validation technique that uses sampling with replacement. This type of validation is most useful for estimating the quantity of a population. When using the bootstrapping validation method, you will take a small sample out of your whole data set. From that small sample, you’ll find the average or another meaningful statistic. You’ll replace the data and include the new statistic that you calculated and then run your model again.
Nested Cross-ValidationMost types of validation techniques are looking to evaluate the error estimation. The nested cross-validation technique is used to evaluate the hyperparameters of your machine learning model. Testing your hyperparameters with this method prevents overfitting. To use this model you nest two k-fold cross-validation loops inside one another. The inner loop is for hyperparameter tuning while the outer loop is for error testing and estimating accuracy.
Choosing the Right ModelThis list of machine learning validation models is not exhaustive, there are many more types of testing models and validation techniques. Each one functions differently and can give you a slightly different insight into your data and machine learning model. And, often, there’s a right validation technique to use and a wrong one. It’s important to evaluate the different validation techniques to make sure you’re picking the right one for your model so you can ensure that it’s error-free. Choosing the right validation model is tricky. It requires an understanding of the data and the machine learning model to make sure you can get the information that you’re looking for. And, it’s not a step you can take lightly or skip. Choosing the right validation technique means you can test your machine learning model and know that it’s secure, free of bias, and reliably returning high-quality output to you.
Insights from Shambhavi Srivastava, Appen’s Data Science & Machine Learning ExpertAdvanced AI and machine learning models become more and more powerful, they tend to become more and more complicated to validate and monitor. Model validation is very critical to ensure a model’s sound performance. According to McKinsey, about 87% of AI proof of concepts (POC) are not deployed in production. Proactive validation of models can help close the gap between model POC’s and production deployment.
Which metrics assess the model?For regression based models, the suggested model validation method would be to use Adjusted R-squared to measure the performance of the model against that of a benchmark. It also tells how well your selected features explain the variability in your labels. For classification, the metric to validate the model’s robustness is the AUC (Area Under the Curve) of a ROC curve (Receiver Operating Characteristics). This metric measures the ability to accurately predict a class in particular.
What type model dimensions to validate?
- Bias error: Is data useful?
- Variance error: Is the model robust?
- Model Fit: Is the model predicting well with new data?
- Model Dimensions: Is new model better than simpler alternatives?
- Bias: Is model bias towards certain variable?