Updated March 22, 2023
Introduction to Test Data Generation
Test Data is any input given to a Machine Learning model to test its performance and reliability. In order to obtain the Machine Learning models with excellent performance, it is important for a Data Scientist to train it with all possible variations of data and then to test the same model even more varied and complicated yet all-inclusive data. Often it becomes difficult to include all the scenarios and variations in the test data that is obtained after the train test split. Hence it becomes important to create a Dataset with all the use cases covered that can best measure our model performance. The process of generating such a dataset is known as Test Data Generation.
Rules of Test Data Generation in Machine Learning
In today’s world, with complexity increasing day by day and delivery time spans reducing, data scientists need to prepare the best performing models as soon as possible. However, models being created only becomes the best performing models once it has been tested on all the kinds of scenarios possible. All these scenarios may not be possible for the data scientist to have with him and hence he may need to create some synthetic data to test the models.
Hence, to create these synthetic datasets, there are certain kinds of rules or guidelines you must keep in mind:
- You must observe the statistical distribution of each feature in the original or the real dataset. Then accordingly we need to create the test data with the same static distributions.
- We need to understand the effects of the interaction that the features have over each other or on the dependent variable. By this, we mean to say that we need to preserve the relations among the variables. Have a look at the univariate, bivariate relationships and try to have the same relationships when creating the test data.
- The data generated should preferably be random and normally distributed.
- In the case of classification algorithms, we need to control the number of observations in each class. We can either have the observations equally distributed to make the testing easy or have more observations in one of the classes.
- Random noise can be injected into the data to test the ML model on anomalies.
- We also need to preserve the scale of values and variations in the features of the test data i.e. the values of the feature should be depicted correctly. E.g. values of age should be around the bracket 0-100 and not some number in thousands.
- We will need an extremely rich and sufficiently large dataset, which can cover all the test case scenarios and all the testing scenarios. Poorly designed test data may not test all possible tests or real scenarios which will hamper the performance of the model.
- We need to generate the dataset large enough so that not only the performance but also the stress testing is done of the model and software platform.
How to Generate Test Data?
Generally, the test data is a repository of data that is generated programmatically. Some of this data may be used to test the expected outcomes of the machine learning model. This data may also be used to test the ability of the machine learning model to handle outliers and unseen situations given as input to the model. It is important to know what kind of test data needs to be generated and for what purpose.
Once we know this, we can follow any of the following methods to generate the test data:
- We can manually generate the test data according to our knowledge of the domain and the kind of testing we need to do on a specific machine learning model. We can use excel to generate these kinds of datasets.
- We can also try and copy huge chunks of data that are available to us in a production environment, make necessary changes to it and then test the machine learning models on the same.
- There are many tools available in the market for free or paid that we can use to create test datasets.
- Test datasets can also be generated using R or Python. There are several packages like faker which can help you in the generation of synthetic datasets.
Advantages of Test Data Generation
Although the test data has been generated by some means and is not real, that is still a fixed dataset, with a fixed number of samples, a fixed pattern, and a fixed degree of class separation. There are still several benefits that the Test Data generation provides.
- Many organizations may not be comfortable in sharing the sensitive data of their users to their service providers as that may violate the security or privacy laws. In these cases, the generated test data can be helpful. It can replicate all the statistical properties of real data without exposing real data.
- Using the generated test data, we can incorporate scenarios in the data which we have not faced yet, but we are expecting or may face in the near future.
- As seen before, the generated data will preserve the univariate, bivariate and multivariate relationships between variables along with preserving specific statistics alone.
- Once we have obtained our method to generate the data, it becomes easy to create any test data and save time on either searching for data or on verifying the model performance.
- The test data would provide the team with much-needed flexibility to adjust the data generated as and when needed in order to improve the model.
To conclude, well-designed testing data allows us to identify and correct serious flaws in the model. Having access to high-quality datasets to test your machine learning models will help immensely in creating a robust and foolproof AI product. The generation of Synthetic test datasets come as a boon in today’s world.
This has been a guide to the Test Data Generation. Here we discuss the rules and how to generate test data with their advantages. You may also have a look at the following articles to learn more –