Understanding Training and Testing Data in Machine Learning

Training and testing data in machine learning are the foundations of effective models and accurate results. Yet all too often, these subjects get confused or conflated, which can result in suboptimal outputs. 

We’ll help clarify the differences between training and testing in machine learning, along with helpful tips, a dive into data splitting, best practices, common pitfalls, and more. 

Introduction to Training and Testing Data

In the machine learning pipeline (preprocessing, training, validation, testing), training and testing are critical to achieving optimal results. Let’s break training and testing data in machine learning down into its component parts and then analyze the differences.

Training data in machine learning

Training data is used to teach the model patterns and relationships. Data should be labeled, and should include diverse and representative samples. 

Testing data in machine learning

Testing data is used to assess a model’s performance after the training portion, and specifically its accuracy, reliability and robustness in real-world conditions. Of course, this data must be different from the data used in the training of the model.

To illustrate the point, say you’re building a machine learning model to classify emails as either “spam” or “not spam.” 

To train the model, you might collect a dataset of labeled emails where each one is marked as “spam” or “not spam.” For example, emails with phrases like “You’ve been hacked” may be labeled as “spam,” while emails with personalized content like “It was great meeting you yesterday” are labeled “not spam.” 

The model analyzes the patterns in these emails and learns the differences between spam and non-spam emails. Thus far, this is all based on the training data.

Once the model is trained, it needs to be tested to ensure it works well on real-world,  unseen data. 

The testing data might be from the same dataset (though not the same actual data) and will be similarly labeled. However the model does not know the labels. 

If the model is fed an email from the testing data, “Your account details are exposed,” it will likely mark it as spam based on its training. This can then be compared to the actual label to assess performance. 

The Importance of Data Splitting in Machine Learning

This highlights the crucial element of data splitting when it comes to testing and training data in machine learning. Splitting your data effectively ensures:

• Objective evaluation of model performance free from bias
• The early detection of overfitting on unseen data
• Improved generalization by replicating real-world scenarios
• The enablement of hyperparameter tuning without impacting final evaluation

The training data is the larger portion of the dataset. That is, if a dataset represents 100% of the data you have available, generally around 80% will be used for training, and 20% for testing. 

Best Practices for Creating Training and Testing Datasets

There are certainly best practices to consider when preparing your training and testing datasets for your machine learning projects. Some of these are:

• Ensure a data splitting ratio that leaves enough data for testing 
• Maintain class balance with random and stratified sampling
• Pay attention to the separation of datasets so that there’s no leakage
• Make sure your preprocessing is consistent across both the testing and training sets
• Confirm the testing set reflects the real-world data likely to be encountered by the model

Common Pitfalls and How to Avoid Them

Common pitfalls and mitigation strategies include: 

Pitfall: Imbalance data splits

How to mitigate: Stratified sampling to ensure class distribution

Pitfall: Overfitting

How to mitigate: Try avoid using the testing set for hyperparameter tuning or repeated evaluation

Pitfall: Not enough testing data

How to mitigate: There will always be the temptation to use more data in training the model; make sure a meaningful portion of the data is left for testing

Pitfall: Mismatched preprocessing

How to mitigate: Maintain consistency across all datasets to ensure robust evaluation and trustworthy results

Pitfall: Bias in data

How to mitigate: Check datasets to ensure they’re representative of real-world conditions

Enhancing Model Performance through Effective Data Management

Effective data management is central to maximizing machine learning model performance. Clean, well-organized data allows the model to learn patterns more effectively. This in turn reduces errors and improves accuracy. 

Preprocessing steps like handling missing values, removing duplicates, and normalizing data also ensure consistency and reliability. And emphasizing data diversity helps the model generalize better, reducing the risk of bias or overfitting. 

Effective data management also governs the organization of data into the appropriate splits for training, validation, and testing – while supporting a structured workflow to ultimately provide clear insights into the model’s capabilities.

FAQ

Q: What percentage of data should be used for training and testing? 

A: Typically, 70% to 80% of the dataset is used for training, with the remainder used for testing in machine learning. This ensures enough data for training and a reliable evaluation of the model’s performance using the testing data.

Q: Can the same data be used for both training and testing?

A: No, the same data should not be used for both training and testing in machine learning, as it can lead to overfitting and an inaccurate assessment of the model’s performance. 

Q: How do you handle imbalanced datasets during training and testing?

A: Use techniques like oversampling the minority class, undersampling the majority class, or applying class weighting to ensure balanced training and testing data.

Q: Why is it important to randomize data when splitting into training and testing sets?

A: Randomizing data when splitting into training and testing sets in machine learning ensures the splits are representative of the entire dataset, avoiding bias and ensuring enhanced generalization.

Meet SQream – Industry-leading GPU accelerated data processing

Handling massive datasets in training and testing data for machine learning means that you and your team need the resources and capabilities to work with massive amounts of information.

This is where GPU acceleration becomes essential. It offers faster data preparation, transformation, and analysis. 

SQream uses the power of GPU acceleration to give you the ability to query even petabyte-scale datasets effortlessly. 

As an advanced data analytics and acceleration platform, SQream will ensure you’re availing yourself of the maximum amount of data – in quantity and quality – to train the best machine learning models possible, and set up your organization for long-term success. 

You’ll be able to shorten your time-to-insight, uncover deeper patterns and trends, and predict outcomes more accurately. All at a fraction of the cost of traditional CPU-based solutions. To learn more, get in touch with the SQream team. 

Summary

We started off noting the importance of the data that’s used in training and testing your model, and the impact this has on model performance. We then looked at the differences between training and testing data in machine learning, the importance of data splitting, best practices and common pitfalls.

Finally, we turned our attention to the practical matters of using data in training and testing machine learning models. Specifically, we explored how SQream’s GPU-accelerated solution can empower AI and ML teams to work with the largest and most complex datasets to ensure comprehensive training and testing, and ultimately achieve exceptional business outcomes.