When it comes to building a machine learning model, all the excitement and energy gets channeled into collecting the data and training the model. What often gets less time in the spotlight is testing the model and validating the results. The right validation techniques help to estimate unbiased generalized model performance and give a better understanding of how the model was trained. You want to make sure that your machine learning model is accurately trained and that it outputs the right data and that your machine learning model’s prediction is accurate when it is deployed to real-world scenarios. Models properly validated are robust enough to adapt to new scenarios in the real world.
Unfortunately, there’s no one validation technique that will work for all machine learning models. Choosing the right validation method requires an understanding of group and time-indexed data.
In this post, we’ll go over the main validation methods and show why it’s important to test and validate your machine learning model outcomes.
The Importance of Model Validation
Validating 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.
One 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.
Validating 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.
While 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 Drift
Concept 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 People
If 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.
No 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 Techniques
There 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 Holdout
The 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.
The resubstitution 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.
A 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 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.
Bootstrapping 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.
Most 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 Model
This 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 Expert
Advanced 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?
One of the known model validation techniques is Cross Validation. This technique consists of a training model and validation on a random dataset multiple times. Each repetition is called fold. A 5-fold cross-validation means that you will train and then validate your model 5 times. A very good option when you have smaller datasets considering multiple folds will cover larger data points. A robust and stable model will have similar performance in every fold.
Bias when using training and validation data
The evaluation of model on training dataset would generate a biased score. Therefore the model is evaluated on the held-out sample to give an unbiased estimate of model skill. Bias systematically underestimate or overestimate the performance of models. Training a model can be divided into two steps first, low-level training and hyperparameter optimization. Both can cause the model to perfectly adapt to the available training set. When the sample sets are smaller we cannot decide how representative the model is and also how well the model can guard against overfitting. We tune the model using different sets of hyperparameters and then selecting the best model. The best model is identified by its test error on the validation test. But this figure of merit (test error) is itself subject to bias and variance. If the validation set is independent than training data the bias will be zero but that doesn’t tackle the issue of variance uncertainty.
What We Can Do For You
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