Training data is used to fit the model. Validation data helps guide model development and selection. Test data is kept separate for a final evaluation of the selected approach.
Why Do We Split Machine Learning Data?
Imagine giving a student the exact questions and answers from an exam during practice, then using those same questions to measure how well the student learned the subject.
A high score would not necessarily tell you how well the student could answer new questions.
A similar issue occurs in machine learning. A model can perform well on observations it has already encountered without performing equally well on new data.
The purpose of splitting data is not simply to follow a standard procedure. It helps separate model fitting and development from independent evaluation.
What Is Training Data?
The training set is the portion of the available data used to fit the model.
During supervised learning, the algorithm uses the training examples to learn relationships between the input features and the target variable.
Predicting Customer Churn
Suppose you have historical customer records containing contract type, monthly charges, tenure, service usage, and a target showing whether each customer churned.
The training portion of those records can be used to fit a classification model that learns patterns associated with the churn outcome.
Because the model is fitted using this data, performance on the training set alone is not enough to establish how well it will generalize.
What Is Validation Data?
Validation data is used during model development to compare candidate models, tune hyperparameters, choose features, set thresholds, or make other modeling decisions.
For example, you might compare several model configurations and use validation performance to decide which configuration should move forward.
Choosing Model Settings
Suppose you are building a decision tree. One configuration produces a very deep tree, while another restricts the tree depth.
Validation performance can help you compare those configurations without repeatedly consulting the final test set.
If you repeatedly use a dataset to choose models and settings, information from its performance is influencing your decisions. That is why it should not automatically be treated as a completely untouched final evaluation set.
What Is Test Data?
The test set is intended to provide a final evaluation of the model or modeling procedure after the main development decisions have been made.
The model is not fitted using the test observations, and the test results should not be repeatedly used to tune the model.
Final Model Evaluation
After comparing several candidate classifiers using training and validation procedures, you select one approach.
You can then evaluate that selected approach on the held-out test set using metrics appropriate for the project.
Depending on the task, these metrics might include accuracy, precision, recall, F1 score, ROC-AUC, mean absolute error, root mean squared error, or other measures.
The appropriate metric depends on the actual prediction problem and the consequences of different kinds of errors.
Training vs Validation vs Test Data
| Dataset | Main Purpose | Used to Fit Model? | Influences Model Choices? |
|---|---|---|---|
| Training | Learn model parameters from data | Yes | Yes |
| Validation | Guide model selection and tuning | Not ordinarily for fitting candidate models | Yes |
| Test | Final evaluation of the selected approach | No | Ideally no |
Exact workflows vary. Cross-validation, nested cross-validation, repeated evaluation, and final refitting can change how the available observations are used. The important principle is to preserve independence between model development and final evaluation.
What Split Ratio Should You Use?
There is no universal percentage that every machine learning project must use.
Examples you may encounter include:
- 70% training, 15% validation, 15% test;
- 80% training, 10% validation, 10% test;
- 70% training and 30% test with cross-validation inside the training data; or
- 80% training and 20% test with cross-validation used for model selection.
The choice depends on factors such as dataset size, model complexity, class balance, evaluation needs, and the modeling workflow.
Treat common ratios as starting points rather than rules. A 70/30 split may be reasonable in one project and wasteful or unreliable in another.
When Is Stratified Splitting Useful?
In classification problems, especially when classes are imbalanced, a purely random split can sometimes produce noticeably different class proportions across datasets.
Stratified splitting attempts to preserve the distribution of the target classes more closely across the resulting subsets.
An Imbalanced Default Dataset
Suppose 10% of observations represent loan defaults and 90% represent non-defaults.
A stratified split can help maintain approximately similar class proportions in the training and test sets.
Stratification does not solve class imbalance itself. It helps create splits that better preserve the target distribution.
Where Does Cross-Validation Fit In?
Instead of relying on one fixed validation set, cross-validation repeatedly divides the development data into different training and validation portions.
In k-fold cross-validation, for example, the data is divided into k folds. The model is trained several times, each time using a different fold for validation and the remaining folds for training.
5-Fold Cross-Validation
The development data is divided into five folds. Five model fits are performed, with each fold serving as the validation fold once.
Performance across the folds can then be summarized to help compare modeling choices.
Cross-validation can make more efficient use of limited data during model development, but it does not automatically remove the need for a genuinely independent final evaluation when one is required.
Preprocessing Can Cause Data Leakage
Splitting the dataset correctly is not enough if information from the validation or test set leaks into the training process through preprocessing.
Consider standardizing a numerical variable using its mean and standard deviation.
If you calculate preprocessing parameters using all observations before splitting, information from the validation or test observations has influenced the transformation applied during training.
Instead, preprocessing steps that learn information from the data should generally be fitted using the appropriate training portion and then applied to the corresponding validation or test data.
This principle can apply to operations such as:
- standardization and normalization;
- imputation;
- feature selection;
- some encoding procedures;
- dimensionality reduction; and
- resampling techniques.
Machine learning pipelines are useful partly because they can help ensure that transformations are learned within the appropriate training portion during validation procedures.
Time-Based Data Needs Special Treatment
Random splitting is not appropriate for every dataset.
When the goal is to predict future observations from past observations, randomly mixing future and past records can create an unrealistic evaluation.
Forecasting Monthly Sales
Suppose you have monthly sales from January 2022 through December 2025 and want to predict future sales.
A realistic evaluation might train on earlier periods and evaluate on later periods rather than randomly distributing months across the training and test sets.
Time-series validation often uses chronological splits, rolling windows, or expanding windows so that the evaluation better reflects how the model would actually be used.
A Practical Model Development Workflow
Define the prediction problem and target variable.
Decide how observations should be separated based on the structure of the data.
Keep the final test data separate from model development.
Fit preprocessing operations using the appropriate training data.
Train candidate models using the training portion.
Use validation data or cross-validation to compare and tune candidate approaches.
Select the modeling approach based on suitable evaluation criteria.
Evaluate the finalized approach on the held-out test data.
Report the evaluation method and metrics clearly.
Common Dataset Splitting Mistakes
1. Evaluating only on the training data
Training performance tells you how the model behaves on data used during fitting. It does not provide an independent estimate of performance on unseen observations.
2. Repeatedly tuning the model using the test set
If test performance repeatedly influences model choices, the test set becomes part of the development process and loses its role as an untouched final check.
3. Preprocessing the full dataset before splitting
Some transformations learn information from the data. Fitting them before the split can leak information across dataset boundaries.
4. Assuming 70/30 or 80/20 is always correct
Split ratios should reflect the amount of available data and the evaluation strategy rather than being selected only because they are common examples.
5. Ignoring class imbalance
A random split can produce problematic class distributions, especially when the minority class is small.
6. Randomly splitting time-dependent observations
If future information ends up influencing a model intended to predict the future from the past, the evaluation may become unrealistically optimistic.
7. Allowing related observations to cross the split
Some datasets contain multiple records from the same person, device, household, patient, or other group. Randomly placing closely related observations into both training and test data can create leakage or an unrealistic evaluation.
The correct split is determined by the real prediction setting, not simply by randomly assigning rows to datasets.
Final Data Splitting Checklist
A strong evaluation begins before the model is trained. How you separate and protect the data directly affects how much you can trust the reported performance.
Not Sure How to Split or Prepare Your Dataset?
Share your dataset structure, target variable, project requirements, software, and modeling objective. The training and evaluation workflow can then be considered in the context of your actual project.