ML Interview Q Series: What is the difference between Test Set and Validation Set?
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Comprehensive Explanation
A test set is the final, unbiased dataset that you use to estimate how well your trained model will generalize to new, unseen data in the real world. You keep the test set aside until all decisions involving training and hyperparameter tuning are complete. This ensures the final reported performance metrics from the test set are a truly unbiased estimate of your model’s performance.
A validation set, in contrast, is used to make choices about the model during development. It aids in activities like hyperparameter selection, model architecture decisions, regularization tuning, and other fine-tuning activities. The model is repeatedly evaluated on the validation set throughout the training phase, and the tuning process continues until you decide on the best model configuration. Because the validation set influences those adjustments, it becomes part of the model selection and can introduce bias if used later for an unbiased performance measure.
In many practical scenarios, you split your initial dataset into training and test sets, and then further split the training portion into training and validation. The training set is used to learn the parameters of your model. The validation set is used to make decisions about model complexity or other hyperparameters. Only once you finalize a model configuration do you evaluate on the test set to get an unbiased performance measure. In cross-validation, you repeatedly split your data into multiple folds, systematically treating one fold as the validation set and the rest as the training set, rotating through all folds for a robust estimate. However, it is still common to keep an additional separate test set out of this cross-validation scheme as a final unbiased measure of performance.
When the dataset is large, having a distinct validation set is often straightforward. For smaller datasets, people might prefer cross-validation to maximize usage of data for both training and validation. Yet, retaining a final test set for the ultimate performance metric remains a critical best practice.
Below is a small Python code example showing how one might create these splits using scikit-learn:
import numpy as np
from sklearn.model_selection import train_test_split
# Suppose X is features, y are labels
X = np.random.rand(1000, 10) # 1000 samples, 10 features each
y = np.random.randint(0, 2, size=1000) # binary classification
# First split off the test set
X_train_full, X_test, y_train_full, y_test = train_test_split(X, y,
test_size=0.2,
random_state=42)
# Further split train_full into train and validation
X_train, X_val, y_train, y_val = train_test_split(X_train_full, y_train_full,
test_size=0.25, # 25% of 80% = 20% of total
random_state=42)
# Now X_train, y_train is used for training
# X_val, y_val is used for validation
# X_test, y_test is used for final evaluation
What happens if you use the test set to tune your model hyperparameters?
Using the test set to guide or influence any step of training, including hyperparameter tuning, will lead to an overly optimistic and biased estimate of the model’s performance on truly unseen data. The test set becomes effectively part of the training process because you are implicitly fitting your choices to that data. Hence, you lose the guarantee of an unbiased final performance measure.
How do you decide on the split ratios for train, validation, and test?
The choice of splitting depends on factors such as dataset size, complexity of the model, and how many hyperparameters you need to tune. A common approach is something like 70% training, 15% validation, 15% test. However, for very large datasets, one can often afford a much smaller test set, since even 5% might be big enough to obtain a robust estimate. For smaller datasets, cross-validation becomes more appealing to maximize data utilization for training and validation, while still keeping a final test set aside.
If you perform k-fold cross-validation, do you still need a separate test set?
Many people prefer to keep a final separate test set even if they use k-fold cross-validation for model selection and hyperparameter tuning. This final test set is never touched during cross-validation. Once you finalize your best model using cross-validation results, you evaluate it on that held-out test set for a genuinely unbiased measure.
Could you use multiple validation sets?
In some cases, you might create multiple validation sets to ensure that you are not overfitting to a single validation set. One approach is to repeatedly shuffle the data and create different train-validation splits (akin to cross-validation). However, maintaining a single final test set that is never used until the end remains crucial to avoid any possibility of bias.
What if your dataset is extremely large? Do you still need a separate validation set?
If you have a massive dataset, you might not need a separate validation set for every training run if you employ other methods for checking overfitting, such as online evaluation or metrics monitored during training (common in deep learning with large sets). Yet, in general, it is still good practice to keep a properly defined validation set or use cross-validation. Even with massive data, it is standard to split out a portion for validation so that you have consistent conditions each time you track model improvements.
Are there scenarios where a validation set may be unnecessary?
If you have a very standardized model building process with limited or no hyperparameter tuning, or you are using a technique like leave-one-out cross-validation extensively, you might not need an extra, dedicated validation set. In such cases, cross-validation results can guide model selection. However, most real-world scenarios do require some form of validation set to systematically tune model complexity and other hyperparameters.
What if the validation set performance is excellent but the test set performance is poor?
This typically indicates overfitting to the validation set. It can happen when you iterate too many times on the same validation set. One approach to mitigate this is to periodically change the validation set or use cross-validation for more robust estimation. A final test set should then detect this mismatch by showing poorer results.
How do you handle real-time model updates where there isn’t a fixed “test set”?
In production systems that continuously collect new data, the notion of a static test set might be less relevant. You might adopt an online evaluation strategy, periodically training or updating the model, then evaluating on a rolling window of future data to simulate real-world performance. Despite this dynamic approach, you still create a concept of a “holdout” set or a slice of time-based data that you do not train on, using it for unbiased performance evaluation.
What is the key takeaway when comparing test sets and validation sets?
The crucial point is that the validation set is part of the model-building process, guiding your decisions about the model architecture or hyperparameters. The test set, however, should remain untouched until you finalize everything else, preserving it for the most unbiased estimate of performance. This distinction helps maintain the integrity and credibility of the results you report.
Below are additional follow-up questions
What if you have a very small dataset and cannot afford to split off a separate test set?
In cases where the dataset is extremely limited, practitioners may feel hesitant to keep a distinct test set because it reduces the already small amount of data available for training. Despite this temptation, it is typically still important to reserve a portion of the data strictly for final evaluation. However, you can mitigate the trade-off by using techniques like cross-validation for model selection, thus ensuring that most of the data gets used for training at some point. You still benefit from the presence of a small holdout set (e.g., a separate test set or a final fold of your cross-validation) as an unbiased performance estimate. If you use every single observation for both training and validation, there is a real risk of overestimating how well your model will do in production.
A potential pitfall is that if every single data point is involved in repeatedly tuning your hyperparameters without a final holdout, the performance metric may be overly optimistic. Another subtle challenge with very small datasets is that random splits can lead to high variance in performance estimates, so you must ensure you do repeated runs or multiple splits to get a more stable assessment.
Can the validation set be combined with the training set for retraining once the model has been selected?
One common practice is to take the final chosen hyperparameters (identified using the training and validation sets) and retrain the model on the combined set of training+validation data to maximize the amount of data used for learning. After this, you still use the held-out test set as your final evaluation. This can boost your final model’s performance a little because it sees more data during training. The important point is that you do not touch the test set throughout this retraining process. The test set remains strictly separate and only comes into play at the very end.
A potential pitfall is inadvertently using the test set data in any capacity (even indirectly) to guide the training or hyperparameter tuning. That would negate the primary reason for having an independent test set in the first place.
Are there scenarios where multiple test sets might be used, each representing different real-world distributions?
Yes, especially when you expect your system to encounter data from different regions or conditions in real-world usage. You might maintain separate test sets for each distribution of interest. For example, in an image classification system, you could have a standard test set plus a specialized test set of images taken under low-light conditions or from a particular camera sensor. This helps you measure performance across various environments and ensures that your model does not degrade significantly in particular scenarios.
A potential edge case is that while you maintain multiple test sets, you might inadvertently tune your model for one distribution after seeing those test results. If you keep repeatedly peeking at multiple test sets during your tuning, you risk overfitting to them in combination. To avoid that, it can be useful to designate one test set as the “primary” metric. The others are used as secondary checks, enabling you to measure performance in different contexts without repeatedly overfitting to every test set.
How do you handle time-series data where future data must not be used for validation or testing?
In time-series tasks, random splitting can violate the chronological order of data. Instead, you might employ a rolling or forward-chaining method for the validation set, ensuring the model does not peek into the future. You typically train on data up to time t, validate on time window t+1, and so on. After you finalize your hyperparameters or model approach, you could test it on a future window of data (time t+2 or beyond) that was never used in training or validation.
A subtle pitfall is incorrectly mixing future time points into the training or validation sets, which leads to data leakage and artificially inflated performance scores. Another challenge is that the distribution might evolve over time. So, you need to consider whether your validation set distribution aligns with the final test window’s distribution. If they differ significantly, you could see a performance drop on the actual test window.
Could you accidentally create a biased validation set even with random splitting?
Yes. Even random splitting can yield a validation set that differs systematically from the broader data distribution, especially if your dataset is not large. This bias might arise due to random chance or insufficient stratification. For classification, a common strategy is stratified splitting, ensuring the proportion of each class is preserved in both training and validation sets. This helps produce more representative splits.
A hidden pitfall is that if you have key subpopulations or rare classes in your data, a naive random split may omit them or severely underrepresent them in the validation set. This can lead to an overly optimistic or pessimistic sense of model performance. Careful splitting strategies (stratification or grouping by certain attributes) can help avoid these issues.
How do data privacy or data partitioning rules affect the creation of validation and test sets?
In some domains—like healthcare, finance, or user data—regulations or compliance rules might require that certain user groups or time periods be protected or not used outside of specified conditions. You must ensure that your splits do not violate these constraints. For example, you might not be allowed to merge data from different regulatory regions, or you must keep certain user data out of training altogether.
If such compliance constraints limit how you split the data, you may end up with smaller or nonrepresentative validation/test sets. The pitfall here is that you might inadvertently produce performance estimates that don’t generalize to broader populations, or you might breach compliance if you aren’t careful about which subsets end up in training versus validation/test. Always double-check the relevant regulations and data governance policies.
How do you handle frequent model retraining when the underlying data distribution shifts or drifts over time?
Data drift—where the real-world data distribution changes—can invalidate the static splits you created initially. In such scenarios, you might adopt a rolling validation/test approach, where you continually update the model on the latest training data and use the most recent data (still unseen by the model) as your test set. Alternatively, you might hold out an always-forward portion of data to test on, ensuring the test set simulates a future distribution that the model has not seen.
A subtlety here is how quickly you rotate or change your validation/test sets. Rotating them too frequently might not allow enough data for robust evaluation, while rotating them too slowly can fail to capture real-time shifts. Another pitfall is continuing to use a fixed old test set that no longer matches the current real-world distribution, leading to an inaccurate sense of performance in production.