ML Interview Q Series: Are there any problems with splitting data randomly into Training, Validation, and Test datasets?
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Comprehensive Explanation
Randomly splitting a dataset into separate Training, Validation, and Test sets may appear straightforward, but there are potential issues that can affect the reliability of your model evaluation. Some of the main considerations include:
Data Leakage. Random splitting can accidentally allow information to leak from the training set to the validation or test sets. When samples are correlated (for example, multiple data points from the same user or from a very similar group), random splitting could place highly similar examples in both training and test sets. This leads to overly optimistic performance estimates because your model sees almost identical examples during training and testing.
Imbalanced Classes. If the dataset is highly imbalanced, purely random splitting may not preserve the class distribution across splits. In practice, you often want each split (train, validation, test) to have similar proportions of each class. Otherwise, your validation or test metrics might not be meaningful. Stratified sampling (preserving class proportions) is often necessary.
Temporal Dependencies. For time series or sequential data, random splitting disregards the temporal order. This can cause unrealistic leakage (where future data appears in the training set) and leads to incorrect performance estimates. Instead, time-based splits are essential to ensure the model is validated and tested on data that chronologically follows the training set.
Distribution Shifts. If the data distribution changes over time or across different conditions, a random split might not reflect how the model will perform in realistic scenarios. Sometimes the final test set should represent a distribution from a different time period or a different set of conditions. A purely random split might not capture that distribution shift.
Data Volume Considerations. If the total dataset is small, a random split can lead to a high-variance estimate of model performance. In such scenarios, cross-validation or repeated splits may be more reliable to ensure that every data point is used for both training and validation at some stage.
Overfitting to the Validation Set. Even if the split is done randomly, repeatedly tuning hyperparameters on the same validation set can inadvertently overfit to that particular subset. This often motivates the use of cross-validation for more robust hyperparameter selection or the creation of multiple validation folds.
Additional Considerations
Cross-Validation. Instead of a single random split, cross-validation techniques (k-fold, stratified k-fold, etc.) provide more robust estimates of performance and reduce variance by averaging results over multiple folds. This is especially useful for smaller datasets or when dealing with class imbalance.
Stratified Splits. For classification tasks with imbalanced classes, stratified splitting ensures that each class is represented in the same proportion in each subset. This is important when evaluating metrics such as precision, recall, or F1 score.
Group Splits. In scenarios where data points belong to groups (e.g., multiple samples from the same user or the same device), one might apply group-based splits to ensure that all samples from a given group appear only in one subset (train, validation, or test). This avoids leakage due to shared characteristics within a group.
Time-Based Splits. For time series, you should split chronologically rather than randomly. This approach prevents training on future data and then testing on past data, which is inherently unrealistic in production settings.
Example of a Simple Random Split with Python:
from sklearn.model_selection import train_test_split
# Suppose X and y are your features and labels
X_train, X_temp, y_train, y_temp = train_test_split(X, y,
test_size=0.4,
random_state=42)
# Now split the temp set into validation and test
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp,
test_size=0.5,
random_state=42)
Although this simple approach may be fine for many datasets, it can pose the issues discussed above if you have class imbalance, time dependencies, or data leakage concerns.
Follow-up Question: What if my data is imbalanced?
When dealing with imbalanced data, random splits may produce training, validation, and test sets where the minority class is too sparse or unevenly represented. This can mislead model training and evaluation, since the model could mostly learn to predict the majority class. To mitigate this, you can use stratified splitting methods that preserve the ratio of classes across each set. Many libraries, such as scikit-learn, provide a StratifiedShuffleSplit or StratifiedKFold to handle this situation. This ensures every subset (train, validation, test) has approximately the same distribution of each class as the original dataset.
Follow-up Question: How should data be split for time series?
For time series or any sequential data, the chronological order of samples is critical. A common mistake is to randomly split the dataset, causing future data to appear in the training set. This results in data leakage and an overly optimistic estimate of your model’s performance. Instead, you should split based on time: train on the earliest portion of the data, validate on the next segment, and test on a later segment. For example, you might use the first 70% of your series as training data, the next 15% as validation, and the final 15% as test. If you want multiple folds for more robust hyperparameter tuning, you can use techniques like rolling or expanding window validation, ensuring that each fold respects time order.
Follow-up Question: How can cross-validation help when there is limited data?
When you have a limited dataset, a single random split can lead to high variance in your performance estimates because a small validation or test set might not represent the broader data distribution. Cross-validation addresses this by splitting the data into multiple folds. Each fold takes a turn as the validation set while the rest serve as the training set. This process yields multiple performance estimates, which you can average to get a more stable measure of model performance. For imbalanced data, stratified k-fold cross-validation is typically used to maintain consistent class proportions in each fold.
Follow-up Question: What is data leakage and how can random splitting exacerbate it?
Data leakage occurs when information from outside the training dataset is used to create the model, inadvertently giving the model undue advantage. Random splitting can exacerbate this if correlated samples—such as multiple observations from the same individual or from identical conditions—are scattered in both training and test sets. The model effectively “sees” a similar sample during training and thus performs well on the test set without genuinely learning generalized patterns. To prevent this, you would use group splits (e.g., GroupKFold or GroupShuffleSplit in scikit-learn), ensuring that all samples from a group belong to only one subset (train, validation, or test).
Follow-up Question: How do I prevent overfitting to the validation set?
When tuning hyperparameters, you often evaluate on the same validation set repeatedly. As you iterate, you might overfit to this specific validation subset. One way to avoid this is to use cross-validation for hyperparameter tuning. By rotating through multiple folds, you reduce the chance of overfitting to a single validation set. Another strategy is to keep a truly held-out test set that you never use during model building or tuning. You only use the test set at the very end to get a final performance estimate.
Below are additional follow-up questions
What if the dataset is extremely large? Is random splitting still a concern?
When you have an extremely large dataset, the intuitive belief might be that random splitting becomes less problematic because the sheer volume of data can diminish sampling bias. While it is true that a large dataset typically reduces variance in performance estimates, random splitting can still lead to subtle issues:
Data Leakage. Even at large scales, correlations (for instance, multiple near-duplicate samples or samples from the same user) may leak across the splits. With more data points, it becomes harder to manually spot such leakages. Unintentionally, the model can learn patterns that do not generalize beyond the immediate dataset.
Imbalance. A massive dataset may have small proportions for minority classes or certain subgroups. If they are very sparse (even in a large dataset, you might have thousands of samples of the majority class and only a handful for the minority), random splitting can lead to subsets that do not accurately capture those rare cases.
Computational Overhead. Handling extremely large data often involves distributed computing or parallelization across multiple machines. If random splitting is done naïvely, you could introduce data fragmentation or misalignment that affects subsequent processing steps (e.g., if different machines handle different partitions without careful synchronization).
A practical approach is to verify that each split retains key statistical properties. For example, if you have known user IDs, you may consider grouping by user to avoid leakage or use a stratified approach if the task is classification. Even though the large dataset helps reduce variance in performance estimates, it does not automatically guarantee that your splits are robust against distribution shifts, data leakage, or class imbalance.
How do we handle multi-label classification scenarios while splitting?
Multi-label classification involves samples that can belong to multiple classes simultaneously (e.g., a movie tagged with both “Action” and “Comedy”). This adds complexity to splitting:
Class Overlap. In a multi-label scenario, a single sample might belong to multiple classes. Random splitting can cause class overlap across subsets without preserving proportional representation of each class pair (or set of classes).
Rare Combinations. Some label combinations might be very rare, and random splitting can inadvertently assign all such samples to the training set or to the validation/test sets, skewing the evaluation.
Stratification. Standard stratified splits (designed for single-label tasks) might not directly apply to multi-label data. Specialized strategies, such as iterative stratification for multi-label data, exist to distribute label combinations more evenly.
Using these specialized stratified approaches is often critical in multi-label settings. They work by attempting to preserve the overall distribution of multi-label combinations, ensuring that each dataset partition sees a reasonable representation of every label combination. This prevents performance metrics from being overly optimistic or pessimistic due to missing certain label co-occurrences.
What if there is concept drift or a shifting data distribution over time?
Concept drift occurs when the relationship between features and the target changes over time. A random split disregards temporal order and can yield overly optimistic performance estimates because it mixes older and newer data:
Chronological Integrity. When the future data distribution is different from the past, a time-aware split is essential. A random approach might put future-like samples into the training set, inadvertently “telling” the model about upcoming changes.
Measuring Real-World Performance. If there is drift, training on older data might not generalize well to future data. By chronologically splitting (for example, training on data from January to September and validating on data from October), you see how your model copes with the shift.
Incremental or Online Approaches. If drift is an ongoing concern, online learning or incremental retraining becomes essential. Rather than a single split, you might employ rolling windows, continually updating the model as data arrives. This ensures the model is always tested on data representing the next chronological chunk.
Ignoring drift leads to a mismatch between offline evaluation and real-world performance. A time-based split or a specialized approach for concept drift mitigation is more reliable in detecting how well the model adapts to changing distributions.
How do we handle multiple data sources or domains when splitting?
When data comes from multiple sources, such as different regions or different devices, a random split may inadvertently mix samples from each source across training, validation, and test sets. This mixing can hide domain-specific issues:
Domain Leakage. If a domain is overrepresented in the training set, the model may learn domain-specific cues that do not generalize to other sources. A random split might accidentally include some portion of every domain in each subset, giving an overly rosy picture of cross-domain performance.
Domain-Specific Performance. When evaluating domain adaptation or generalization, you often want to reserve at least one entire domain exclusively for the test set. This allows you to see how the model behaves when confronted with truly unseen conditions.
Fairness Concerns. Different data sources might represent different demographic groups or usage contexts. A random split might not ensure equitable distribution of these groups across splits. If fairness or bias is a concern, you might create domain-based or group-based splits, or at least stratify with respect to domain indicators.
In practice, if your goal is robust cross-domain generalization, you might hold out entire domains for testing. Alternatively, you might split each domain separately, train on a subset of each domain, validate on a different subset, and test on a domain not seen during training.
Is there a scenario where random shuffling in time series tasks is acceptable?
Generally, for time series, you avoid random shuffling because it can leak future information into the training set. However, certain scenarios might allow partial or controlled randomization:
Stationary Processes. If the time series is truly stationary and has no temporal autocorrelation beyond a short window, some researchers argue that random segments (with enough spacing to avoid overlap) could be acceptable for training. Still, final validation or test sets are usually best kept in chronological order to mimic real-world deployment.
Data Augmentation. Sometimes, segments of the time series can be shuffled in a data augmentation phase, but you must carefully separate the segments that belong to validation or test sets to avoid data leakage.
Specific Domains. In certain domains (like controlled experiments with randomized measurements), time dependencies may be minimal. If the data generation process is effectively “reset” frequently, partial shuffling might not lead to severe leakage. Even then, you should confirm that future states do not systematically differ from past states.
When in doubt, default to a time-based split. Shuffling in time series tasks typically introduces more risk than reward, unless you have well-justified reasons and your domain data strongly supports stationarity.
How should we handle the final test set when we perform repeated runs or multiple cross-validations?
Repeated runs and multiple cross-validation folds help produce robust estimates of model performance. However, this can lead to confusion about how to properly handle a final test set:
True Hold-Out. Ideally, the final test set is a single partition that remains unseen until all model selections and hyperparameter tuning are completed. If you incorporate the test set into repeated runs or cross-validation folds, it is no longer a pure hold-out.
Variance in Results. Repeated random splits for cross-validation can yield slightly different metrics each time, especially in smaller datasets. You can average the performance across runs for a more stable metric. The test set, however, remains separate and is used for a final unbiased estimate.
Overfitting to Validation Protocol. If you run extensive cross-validation and choose a model based on the aggregated results, the final test set should not influence any decisions. Even partial exposure or repeated usage of the test set for model tuning can lead to overfitting to that set.
A common pitfall is using the test set repeatedly for iterative model improvements. This effectively turns your test set into a secondary validation set. The best practice is to finalize your model after cross-validation (or multiple runs) on training/validation folds, then do exactly one test set evaluation at the end to avoid bias in your performance estimation.
How do we handle multi-run scenarios where each run uses a different random train/validation split?
When dealing with multiple runs—each with its own random split of train and validation—researchers and practitioners often wonder how to best interpret or combine these results:
Average Performance. One approach is to average metrics (e.g., accuracy, F1 score, RMSE) across all runs. This gives an estimate of how stable the model is with respect to different partitions of data. It also helps reduce variance in estimates that arises from a single split.
Variance or Confidence Intervals. Reporting only the average metric is incomplete. Including standard deviation, confidence intervals, or percentile intervals for the metric gives a clearer picture of model robustness. Sometimes, you might find that certain splits cause significantly worse performance, pointing to potential distribution shifts within the dataset.
Potential Overfitting to Splits. If you frequently choose the best run out of many based on validation performance, you might inadvertently overfit to a random set partition. A safer practice is to run cross-validation or repeated splits, average the performance, and only then pick final hyperparameters or model configurations based on aggregated results.
In real-world practice, it is common to do multiple runs with different seeds to ensure that your model’s performance is not an artifact of a single random split. After you confirm stable performance across these runs, you then use a final hold-out test set to measure real-world generalization performance.