Training vs. Testing Data: How to Prevent Model Memorization in Machine Learning

Picture this: You’re tutoring a student for an upcoming math test. You help them solve dozens of practice problems over several days, and by the end, they’re getting every problem right. You feel confident they’ve mastered the material.

However, on test day, they struggle when faced with new problems that require applying the same concepts in slightly different ways. They haven’t truly learned the underlying principles – they’ve just memorized the specific practice problems you worked on together.

Anyone who’s taught or studied can relate to this scenario, and it perfectly illustrates the central challenge in machine learning: the difference between memorization and generalization. This is the core reason we need both ML training and testing phases.

In our ongoing exploration of mental models in machine learning, we’ve already explored supervised vs. unsupervised learning, classification vs. regression, and prediction vs. inference. Now, we’re exploring another concept: the difference between training and testing data, and why this split is essential for developing models that perform in the real world.

The Fundamental Divide: Training vs. Testing

Just like human learners, machine learning models can fall into the trap of memorizing rather than truly understanding. Think of the model as a student and the data we feed it as the curriculum. If we judge the model’s performance solely on the material it’s already seen, we can’t be sure if it’s learning generalizable patterns or just memorizing specific examples.

To ensure the model learns effectively, we separate the data into two main groups:

  • Training Data: This is the information the model uses to learn patterns. It’s like the practice problems a student works through while studying.
  • Testing Data (sometimes called “holdout data”): This is new data the model has never seen before. It helps us evaluate how well the model performs on unfamiliar examples – like a student’s actual exam.

By dividing the data this way, we can see if the model is truly learning or just memorizing the examples it was given.

Note: While we often say that machines “learn,” it’s important to remember that this isn’t the same as human learning. In machine learning, “learning” refers to a model adjusting its parameters – the internal settings that help it make predictions or decisions – based on patterns found in the data. This process allows the model to improve its ability to make accurate predictions, but it doesn’t involve true understanding or conscious thought like human learning.

Why This Split Matters

I’ve seen teams get excited about a model’s performance only to be disappointed when it’s deployed to production. Almost invariably, this happens because they evaluated the model solely on data it was trained on.

Think about what happens when a model sees the same data during both training and testing:

  1. It might learn specific peculiarities or random noise (irrelevant variations) in the training examples rather than the underlying patterns that actually matter. For example, it might notice that in your training data, houses with blue front doors happened to sell for more, even though that’s just a coincidence in your sample.
  2. If it has enough complexity, it could essentially ‘memorize’ the training data instead of learning general rules. Just like a student who memorizes that ‘7 × 8 = 56’ without understanding multiplication principles, a complex model might memorize that ‘House #127 sold for $350,000’ without learning why.
  3. The performance metrics will be artificially inflated, giving a false sense of how well the model will perform on new data. It’s like a student who gets 100% when retaking the same quiz they’ve already seen the answers to, versus their performance on a new quiz with different questions on the same topics.

I once worked with a retail company that built a customer churn prediction model with 97% accuracy in their initial testing. The team was ready to celebrate until I pointed out they had inadvertently included the customer ID field in their model features. The model wasn’t learning patterns about customer behavior but memorizing which specific customer IDs had churned in the past!

This model would be useless for predicting new customers’ behavior when deployed. After properly removing the customer ID field and evaluating genuinely unseen data, the model accuracy dropped to 76% – a much more realistic assessment of its capabilities.

Let’s walk through a simple, concrete example:

Imagine you’re building a model to predict house prices. You have data on 1,000 recently sold homes, including features like square footage, number of bedrooms, neighborhood, and the final sale price.

You designate 800 of these homes as your training data and the model analyzes relationships between features (like square footage, number of bedrooms, and location – these are the characteristics or “inputs” we use to make predictions) and the target variable (sale price – this is what we’re trying to predict or “output”). You set aside the remaining 200 homes as testing data. Once your model has learned from the training data, you ask it to predict prices for these 200 homes and compare its predictions to the actual sale prices.

The key insight: Those 200 homes in the testing set were never used during training. The model has never “seen” them before, so its performance on these examples gives you a much more realistic assessment of how it will perform in the real world when predicting prices for entirely new homes.

Note: While we’ve described the core concepts of data division here, in practice, machine learning tools and algorithms are used to split and manage the data in more specific ways. We’ll explore the technical details of these processes in another series outside of our Mental Models discussion.

Real-World Examples of Training and Testing

This training/testing divide appears in countless real-world scenarios:

Email spam filters:

  • Training: The model learns from millions of emails manually labeled as “spam” or “not spam.”
  • Testing: Engineers evaluate how well the model classifies new emails it hasn’t seen before

Autonomous vehicles:

  • Training: The system learns from millions of driving scenarios and how human drivers responded
  • Testing: Engineers evaluate the system’s decisions in new driving situations, often in simulations first and controlled real-world environments later

Medical diagnosis:

  • Training: The algorithm learns patterns from thousands of labeled medical images
  • Testing: Researchers assess its accuracy on a separate set of images from different patients

In each case, the testing phase provides a reality check that helps us understand how the model will perform in production.

How to Split Your Data Properly

The standard approach to splitting your data is:

  • 70-80% for training
  • 20-30% for testing

But there’s more to proper splitting than just these percentages. Here are some principles I’ve learned over the years:

  1. The split must be random (with caveats)
    In most cases, you want a random split to ensure both sets represent the overall data. However, there are important exceptions:

    • For time-series data (like stock prices or weather), you typically want to train on earlier data and test on later data, as this mimics how the model will be used in practice.
    • If you have distinct groups in your data, you might want to ensure they’re proportionally represented in both sets (stratified sampling – this means maintaining the same proportion of each group in both training and testing data).
  2. Testing data must remain untouched until the very end
    I can’t stress this enough: You must not use the testing data in any way during model development. Not for feature selection (choosing which inputs to include), not for parameter tuning (adjusting the model’s settings), and not for anything until the final evaluation. I’ve seen teams repeatedly peek at their test set performance while iterating on their models, essentially “fitting to the test set” over time. This defeats the entire purpose of having separate data for testing – it’s like secretly studying the actual exam questions before taking the test.
  3. The testing set should represent future data the model will encounter
    If your production environment has characteristics different from your historical data, your test set should reflect those differences. For example, if you’re building a fraud detection system that will be deployed globally but your historical data is mainly from North America, you should ensure your test set includes examples from other regions to assess the model’s future performance.

Validation: The Middle Ground

But wait – if we can’t look at the test set during development, how do we iteratively improve our model?

This is where a third data split comes in: the validation set.

Here’s the expanded approach many practitioners follow:

  • 60-70% for training
  • 15-20% for validation
  • 15-20% for testing

The validation set serves as a proxy for the test set during development. You train your model on the training data, tune it based on its performance on the validation data, and only use the test data for the final evaluation once your model is built.

Think of it like having practice quizzes (validation) between study sessions (training) and the final exam (testing). The practice quizzes help you gauge your progress and adjust your study methods without revealing the actual exam questions.

This three-way split has saved me countless times from over-optimistic performance estimates. On a fraud detection project, our model achieved 92% accuracy on the validation set after multiple iterations. When we finally ran it on the untouched test set, we got 91% – a sign that our validation approach was working well and our model would likely perform similarly in production.

Cross-Validation: Going Beyond Simple Splits

Even setting aside 15-20% for validation for smaller datasets means losing valuable training examples. This is where cross-validation becomes invaluable.

In k-fold cross-validation (typically with k=5 or k=10):

  1. You divide your training data into k equal parts (folds)
  2. You train k different models, each using k-1 folds for training and the remaining fold for validation
  3. You average the performance across all k models to get a more robust estimate

This approach makes better use of limited data by ensuring every example serves as both training and validation at different times.

Cross-validation is particularly helpful in healthcare projects where data is often limited. In one cancer prediction model, using 10-fold cross-validation rather than a simple validation split improved our final model’s performance by about 4% – a significant gain in a high-stakes domain.

Common Pitfalls in Training and Testing

Over the years, I’ve seen teams fall into several common traps when implementing the training/testing paradigm:

Pitfall 1: Data Leakage

This occurs when information from the test set inadvertently “leaks” into the training process. Data leakage means that information that wouldn’t be available in real-world usage somehow influences the training of your model.

I once consulted for a company predicting equipment failures where their preprocessing pipeline normalized all the data together before splitting it. This meant information about the test set’s distribution influenced the training data, creating artificially high performance that didn’t translate to production.

The solution: Always split your data before any preprocessing steps that look at the distribution of values.

Pitfall 2: Temporal Misalignment

In time-sensitive applications, testing must reflect how the model will be used in the real world, where you’ll only have past data to predict the future.

A retail forecasting model I helped develop performed well when evaluated on randomly selected test days but failed dramatically in production. We discovered that our random splitting had allowed the model to use future information to predict the past! We got a much more realistic performance assessment when we correctly restructured the evaluation to predict forward in time only.

Pitfall 3: Overfitting to the Validation Set

If you iterate too many times against the same validation data, you risk tuning your model to its peculiarities – essentially overfitting to the validation set. This happens because you’re making too many adjustments based on the same validation examples, causing your model to learn patterns specific to this particular validation set rather than truly generalizable patterns.

One solution is nested cross-validation (where you use multiple layers of cross-validation). Another approach is to refresh your validation data periodically if you have sufficient data.

When Your Model Underperforms: Next Steps

After evaluating your model on validation or test data, you might find it’s not performing as well as you hoped. This is actually a normal part of the machine learning process! Here’s what experienced practitioners typically do:

  1. Check for data issues first: Before changing your model, verify that your data is clean and correctly processed. Often, performance problems stem from data quality issues.
  2. Analyze error patterns: Look at where your model is making mistakes. Are there specific types of examples it struggles with? This can provide clues about what to fix.
  3. Address underfitting: If your model performs poorly on both training and validation data, it might be too simple to capture the patterns (underfitting). Consider:
    • Adding more features
    • Using a more complex model
    • Reducing regularization (loosening the restrictions that prevent the model from becoming too complex) if you’re using it
  4. Address overfitting: If your model performs well on training data but poorly on validation data, it’s likely memorizing rather than generalizing. Consider:
    • Collecting more training data
    • Simplifying your model
    • Adding regularization (applying constraints to prevent the model from becoming too complex)
    • Using early stopping based on validation performance
  5. Iterate and refine: Machine learning is an iterative process. Use what you learn from each attempt to improve your next approach, but always maintain that strict separation between training, validation, and test data.

Remember that real-world performance rarely matches the neat examples in tutorials. Even experienced data scientists go through multiple iterations before finding a solution that works well.

What Makes a “Good” Model?

A common question is: “What level of accuracy (or other metric) makes a model good enough?” The answer isn’t as simple as a universal threshold because what counts as good performance depends heavily on:

  1. Your specific problem: In medical diagnosis, 85% accuracy might be dangerously low, while in predicting customer preferences, it could be impressive.
  2. The baseline comparison: A simple model that always predicts the majority class) the most common outcome might achieve 90% accuracy on imbalanced data – your model should significantly outperform this baseline to be valuable.
  3. Business requirements: Sometimes a 70% accurate model that can explain its decisions is more useful than a 95% accurate “black box.”
  4. The state-of-the-art: In mature fields like image recognition, performance below 95% might be disappointing, while in complex domains with limited data, 75% could represent a breakthrough.
  5. Improvement potential: A model that’s 82% accurate but stable and maintainable might be preferable to one that’s 85% accurate but fragile or resource-intensive.

Rather than fixating on arbitrary thresholds, focus on whether your model provides meaningful value above simpler alternatives, meets the specific needs of your application, and makes errors you can live with given the consequences.

The most important question isn’t “Is my model good?” but rather “Is my model good enough for this specific use case?”

The 3Rs of Effective Model Evaluation

Through trial and error, I’ve found that successful model evaluation consistently follows what I call the “3Rs” framework – principles that experienced practitioners often apply:

Representative: Your training, validation, and test sets should all represent the data your model will encounter in the real world.

Random (with purpose): Randomization prevents bias in your splits, but it should be applied thoughtfully with domain knowledge in mind (e.g., keeping time order when relevant).

Rigorous: Maintain strict separation between your data sets and be disciplined about not letting test data influence the training process.

One financial services company I worked with followed these principles religiously and built a credit risk model that performed within 1.5% of its test metrics when deployed to production – a remarkable achievement in an industry where performance often degrades significantly in real-world conditions.

Bringing It All Together

The training/testing divide might seem like a technical detail, but it’s one of the most fundamental concepts in machine learning. It addresses the core challenge of creating models that generalize rather than memorize.

To recap the mental models we’ve covered so far:

These frameworks provide a foundation for approaching machine learning projects thoughtfully, regardless of which specific algorithms you use.

In our next article, we’ll explore “Bias vs. Variance: The Fundamental Tradeoff in Machine Learning,” examining how models can fail in two fundamentally different ways and how to find the right balance for your problem.

What challenges have you faced when splitting data for training and testing? Have you ever been surprised by a model’s performance drop when moving from validation to production? I’d love to hear about your experiences in the comments below.