Top difference between training data and testing data

When I talk about machine learning, what pops out of your brain? We are frequently asked about the distinction between training and testing data. So, we decided to explain it in detail through this blog. Understanding the disparity between these two data types and utilizing them appropriately is essential.

Knowing the difference between these two data types ensures that the machine-learning model is reliable, accurate, and effective.

Read more about AI and ML in software testing here: https://testsigma.com/blog/ai-and-ml-in-software-testing/

This blog post explores the primary purposes of training and testing data, the techniques used to prepare them, and the potential pitfalls that can arise when misusing them and about automation of the data.

What is Training Data?

Do you know: The training data is always correct!

Machine learning algorithms learn from data in datasets. They find patterns in the data, develop an understanding of the data, make decisions based on the data, and evaluate the accuracy of their choices.

In machine learning, datasets are typically split into two subsets: training and testing data. The training data is used to train the machine learning algorithm. The testing data is used to evaluate the accuracy of the trained algorithm. The training data should represent the data the algorithm will encounter in the real world. 

What is Testing Data?

Testing data is like a box of chocolates. You never know what you’re going to get.

Once you have trained your machine learning model on a dataset, you must test it on unseen data to evaluate its performance. This unseen data is called the testing data. This is similar to the test data used in software testing, just the context is different here. In software testing, we use test data to ensure the software works well for given data. In machine learning, we use testing data to ensure the model works for the given testing data. The testing data should meet two criteria:

1. It should represent the actual dataset that the model will be used on. This means that the testing data should have the same distribution of features as the actual dataset.
2. It should be large enough to generate meaningful predictions. This means the testing data should be large enough to provide a statistically significant test of the model’s performance.

The testing data should be new, “unseen” data the model has not seen before. This is because the model will already have learned the patterns in the training data, so testing its ability to generalize to new data is essential.

The testing data can be used to evaluate the model’s accuracy, robustness, and fairness. It can also be used to identify areas where the model needs to be improved.

Splitting the data into 80% training data and 20% testing data is common in data science. This means that 80% of the data will be used to train the model, and 20% will be used to test the model.

Why do we need training and testing data?

Here are some reasons why we need training and testing data in machine learning:

“Training data teaches a machine learning model how to behave while testing data evaluates how well the model has learned.”

1. Training data is used to teach the machine learning model how to make predictions or perform a desired task. It is typically labeled, which means that the model’s output is known for each data point. The model learns to identify the patterns in the data and make predictions based on those patterns.

Training data is like the textbook that a student uses to learn a new subject. The book contains the information the student needs to know, and the student learns by reading the text and doing the exercises.

2. Testing data is used to evaluate the machine learning model’s performance. It is typically different from the training data and not labeled. This means the model’s output is unknown for each data point. The model’s ability to make accurate predictions on the testing data is evaluated.

Testing data is like the test that a student takes to assess their knowledge of the subject. The test contains questions that the student needs to answer, and the student’s performance on the test is used to evaluate their understanding.

By using training and testing data, we can ensure that the model can make accurate predictions on new data it has not seen before.

Why Knowing the Difference is Important?

The difference between training data and testing data is that training data tells you how to build a model, and testing data tells you how to break it.

The difference between training data and testing data is important because it helps to prevent the model from overfitting. Overfitting occurs when the model learns the training data too well and is unable to generalize to new data. This can happen if the training data is too small or if it is not representative of the real world.

By using a separate testing data set, the model can be evaluated on data that it has not seen before. This helps to ensure that the model is able to generalize to new data and make accurate predictions.

How Training and Testing Data Work with Example?

Here is an example of Training Data:

If you train a model to classify images of flowers and fruits, the training data would consists of image labeled as “flowers” or “fruits.” The model would learn to identify the features distinguishing flowers from fruits by looking at the labeled data.

Good quality of the training data is essential for the machine learning model’s performance. If the data is not representative of the real world, the model cannot make accurate predictions. The data should also be large enough to allow the model to learn the patterns in the data.

Properly selecting and preparing training data is necessary for machine learning. This can enhance the performance of your models and ensure their readiness for real-world applications.

Let us see an example for Testing Data:

If you train a model to classify images of flowers and fruits, the testing data would consist of images the model has yet to see. The model would be evaluated on its ability to classify these images correctly.

The testing data quality is essential for the machine learning model’s performance, The data should represent the real world and be large enough to evaluate the model thoroughly.

Lеt mе еxplain to you with another simplе еxamplе: 

Take testing data for a machine learning model that is used to predict the price of houses. The training data might consist of historical data on house prices, such as the house size, the number of bedrooms, and the house’s location.

The testing data would consist of new data on house prices that the model has not seen before. The model would be evaluated on its ability to predict the prices of these houses.

Training Data vs. Testing Data

Feature	Training Data	Testing Data
Purpose	Training data is used to train the machine-learning model. The more training data the model has, the better it can make predictions.	Testing Data is used to evaluate the performance of the model.
Exposure	The model can learn from the training data and improve its predictions.	The testing data is not exposed to the model before evaluation. This ensures the model cannot memorize the testing data and make perfect predictions.
Distribution	The distribution of the training data should be similar to the distribution of the real-world data that the model will be used.	The distribution of the testing data is entirely different from the real-world data.
Use	Training data is used to prevent overfitting.	The testing data is used to evaluate the model’s performance by making predictions on it and comparing the predictions to the actual labels.
Size	The training data is typically larger. This is because the model needs to see many examples of input data to learn how to make accurate predictions.	The size of the testing data is typically smaller than the training data because the testing data is used to evaluate the performance of the model that has been trained on the training data.

How are Training and Testing Data used in Test Automation Tools?

No wonder Training and Testing data are used in Test automation tools. This is to improve the accuracy and reliability of the tests.

Training data is used to train the test automation tool on the specific application or system that is being tested. This helps the tool to learn the expected behavior of the application and to identity any potential defects.

Testing data is used to evaluate the performance of the test automation tool. This helps to ensure that the tool is able to find defects and that it is not overfitting to the training data.

Here are some simple explanations of how training and testing data are used in test automation tools:

• Training data teaches the test automation tool how to interact with the application or system under test. It should represent the real world and large enough to allow the tool to learn the patterns in the application’s behavior.
• Testing data is used to evaluate the performance of the test automation tool. It should be different from the training data and not labeled. This ensures that the tool is balanced with the training data and can find defects in new data.

By using training and testing data, you can build test automation tools that are more accurate and reliable.

Conclusion

As Andrew Ng said, “The training data is the food for the model, and the testing data is the dessert.”

Training data and Testing data are the most essential to understand to build accurate and reliable machine-learning models. By carefully selecting and preparing the data, you can improve your models’ performance and ensure they are ready for real-world use.

Frequently Asked Questions

How can you improve the training and testing data?

Improving the training and testing data can be done by increasing the diversity of the data sources and by carefully selecting and preprocessing the data to remove any biases or noise. Also, incorporating data augmentation techniques can help generate more varied and representative examples for training and testing.

How do you separate training and test data sets?

To separate training and test data sets, you can randomly split the data into two sets using a specific ratio, such as 70% for training and 30% for testing.