Machine learning models are only as good as the data used to build and evaluate them. But one of the most important concepts beginners often struggle with is the difference between training data and testing data.

If a machine learning model is trained and tested using the same data, it may appear highly accurate simply because it has already seen the answers. That does not necessarily mean the model can make reliable predictions on new, unseen data. This is why datasets are separated into different subsets during the machine learning workflow. (IBM)

In this guide, we will explain training data vs testing data, how they work, why the separation matters, common train-test split ratios, the role of validation data, overfitting, data leakage, and practical examples.

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What Is Training Data?

Training data is the portion of a dataset that a machine learning model uses to learn patterns and relationships between inputs and outputs.

During training, the model processes examples and adjusts its internal parameters—such as weights—to reduce prediction errors. Google defines a training set as the subset of a dataset used to train a model. (Google for Developers)

For example, imagine you are building an email spam detection system.

Your dataset might contain:

Email	Label
“Win a free iPhone!”	Spam
“Meeting scheduled for 3 PM”	Not Spam
“Claim your cash reward”	Spam
“Your order has shipped”	Not Spam

The model uses these examples as training data to learn patterns associated with spam and legitimate emails.

Over time, it may learn that certain words, phrases, senders, links, or email characteristics are associated with spam.

The purpose of training data

Training data helps a model:

• Learn patterns
• Identify relationships between variables
• Adjust model parameters
• Reduce prediction errors
• Learn how inputs relate to expected outputs
• Build the ability to make predictions

However, training performance alone does not tell us how well the model will perform on new data.

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What Is Testing Data?

Testing data is a separate portion of the dataset that is kept away from the model during training.

After the model has been trained, the test dataset is used to determine how well the model performs on previously unseen examples.

Google describes the test set as a separate dataset used for evaluating a trained model, with the goal of measuring performance on examples the model has not seen during training. (Google for Developers)

Think of it like an examination.

Training data = Study material

The model learns from it.

Testing data = Final examination

The model must demonstrate what it has learned without having access to the answers beforehand.

This separation provides a much more realistic estimate of how the model may perform when it encounters new data in production.

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Training Data vs Testing Data

The simplest way to understand the difference is:

Factor	Training Data	Testing Data
Primary purpose	Teach the model	Evaluate the model
Used during model training	Yes	No
Used to learn parameters	Yes	No
Model has seen the examples	Yes	Ideally no
Used for final evaluation	No	Yes
Helps identify generalization	Indirectly	Yes
Should influence model decisions	Yes	No
Example	Historical labeled emails	Previously unseen emails

The key principle is separation.

The model should not learn from the test dataset before the final evaluation. Otherwise, the test score can become overly optimistic.

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Why Do We Need Training and Testing Data?

Suppose you give a student the exact questions and answers before an exam.

The student may score 100%.

But does that prove they understand the subject?

Not necessarily.

They may simply have memorized the answers.

Machine learning models can behave similarly.

If you train and evaluate a model using exactly the same examples, the model may achieve excellent performance because it has already encountered those examples. This is one reason testing on unseen data is essential. (Scikit-learn)

The real goal of machine learning is not:

“Can the model remember the training examples?”

The goal is:

“Can the model make accurate predictions about new examples?”

This ability is known as generalization.

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What Is Generalization in Machine Learning?

Generalization refers to a model’s ability to perform well on data it has not previously encountered.

For example, suppose you train a house-price prediction model using historical data.

The training dataset contains:

• Location
• Number of bedrooms
• Area
• Property age
• Amenities
• Historical sale price

The model learns relationships between these variables and property prices.

Now you give it information about a house that was not included in the training dataset.

If the model produces a reasonable prediction, it is generalizing well.

A model that performs extremely well on training data but poorly on new data has a generalization problem.

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What Is a Train-Test Split?

A train-test split means dividing the available dataset into separate portions.

For example:

Complete Dataset
       |
       +----------------------+
       |                      |
   Training Data          Testing Data
      80%                     20%
       |                      |
 Model learns            Final evaluation

Scikit-learn’s train_test_split() function, for example, provides a standard way to randomly divide data into training and testing subsets. Its default test_size is 0.25 when neither train_size nor test_size is specified. (Scikit-learn)

A common manually selected split is:

80% Training + 20% Testing

For a dataset containing 10,000 records:

• Training data = 8,000 records
• Testing data = 2,000 records

However, 80/20 is not a universal rule. The appropriate split depends on the dataset size, problem, data distribution, and evaluation requirements.

Google’s machine-learning guidance, for example, illustrates both 80/20 and three-way splits such as 70/15/15. (Google for Developers)

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Common Training and Testing Data Split Ratios

There is no single ratio that works for every machine learning project.

Some commonly used approaches include:

Training	Validation	Testing	Typical use
80%	—	20%	Simple train-test workflow
70%	15%	15%	Train/validation/test workflow
80%	10%	10%	Three-way split
60%	—	40%	Larger evaluation set in some scenarios

Google’s documentation demonstrates a 70% training, 15% validation, and 15% testing arrangement as one possible three-way split. (Google for Developers)

Google’s News Initiative training material also describes an 80/10/10 split as an example for its AutoML Vision workflow. (Google News Initiative)

The important point is not to blindly follow a particular percentage.

Instead, make sure your test set is:

• Large enough to provide meaningful evaluation
• Representative of real-world data
• Separate from training examples
• Free from duplicates or leakage
• Consistent with the type of data expected in production

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Training Data vs Validation Data vs Testing Data

In many machine learning projects, the dataset is divided into three parts rather than two.

These are:

1. Training data
2. Validation data
3. Testing data

The workflow looks like this:

              Complete Dataset
                    |
        +-----------+-----------+
        |           |           |
      Train      Validate      Test
       70%         15%         15%
        |            |           |
        ↓            ↓           ↓
     Learn       Tune Model   Final Check

Let’s understand each one.

1. Training Data

Used to teach the model.

The model learns its parameters from this dataset.

2. Validation Data

Used during development to compare model configurations and tune choices such as:

• Hyperparameters
• Model architecture
• Feature selection
• Regularization
• Training duration

Google describes validation data as being used for initial evaluation while the model is being developed, while the test set is reserved for the final evaluation. (Google for Developers)

3. Testing Data

Used for the final evaluation of the selected model.

Ideally, the test set remains untouched until the model-development process is finished.

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A Real-World Example

Imagine a company wants to build an AI model that detects whether a medical image contains a particular condition.

Suppose the company has 100,000 labeled images.

A possible split could be:

• 70,000 training images
• 15,000 validation images
• 15,000 testing images

Step 1: Training

The model learns from the 70,000 training images.

Step 2: Validation

Developers evaluate different versions of the model using the 15,000 validation images.

They might change:

• Learning rate
• Model architecture
• Number of layers
• Batch size
• Regularization

Step 3: Final testing

Once the model configuration is finalized, the 15,000 test images are used for final evaluation.

This gives the development team a better indication of how the model may perform on unseen examples.

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What Happens If You Test Using Training Data?

This is one of the most important mistakes to avoid.

Suppose your model achieves:

Training accuracy: 99.8%

That sounds impressive.

But if the model achieves only:

Test accuracy: 72%

there is a major problem.

The model has likely learned the training examples too closely rather than learning patterns that generalize to new data.

This phenomenon is called overfitting. IBM describes overfitting as a situation where a model fits training data too closely and consequently performs poorly on data outside the training set. (IBM)

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What Is Overfitting?

Overfitting occurs when a machine learning model learns the training dataset—including its noise or specific details—too closely.

For example:

Training Accuracy: 99%
Testing Accuracy: 70%

This large gap may indicate that the model is overfitting.

The model has essentially become very good at recognizing its training examples but struggles with unfamiliar examples.

Example

Imagine training a model to recognize cats.

Your training images contain:

• Cats on sofas
• Cats in gardens
• Cats indoors
• Cats outdoors

But most cat photos in the training set have blue backgrounds.

The model could accidentally learn:

“Blue background = cat.”

Instead of learning:

“These visual characteristics represent a cat.”

When it encounters a cat photographed against a red background, it may fail.

This is why representative training and testing data are important.

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What Is Underfitting?

Underfitting is almost the opposite problem.

A model underfits when it is too simple or has not learned enough from the data.

For example:

Training Accuracy: 65%
Testing Accuracy: 63%

Both results are poor.

This suggests that the model is struggling even with the training data.

IBM notes that underfitting can occur when a model is too simple or when insufficiently informative features prevent it from capturing important relationships in the data. (IBM)

The goal is to find a balance:

Underfitting ← Good Generalization → Overfitting

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Why Test Data Must Remain Unseen

The test set is valuable because it acts as an independent evaluation.

If developers repeatedly examine the test results and modify the model based on those results, the test set effectively becomes part of the development process.

This can reduce its value as an unbiased final evaluation.

Google specifically notes that validation and test sets can become less reliable when repeatedly reused, making fresh evaluation data important. (Google for Developers)

A good workflow is therefore:

Training Data
      ↓
Train Model
      ↓
Validation Data
      ↓
Improve / Tune Model
      ↓
Finalize Model
      ↓
Test Data
      ↓
Final Evaluation

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What Is Data Leakage?

Data leakage occurs when information from outside the training data improperly influences the model during development.

One common example is accidentally allowing information from the test set to influence preprocessing or feature selection.

Scikit-learn recommends splitting training and testing data before fitting preprocessing transformations, because learning preprocessing parameters from the complete dataset can allow test information to leak into the training process. (GitHub)

Incorrect approach

Complete Dataset
      ↓
Normalize entire dataset
      ↓
Split into train/test

The normalization step has already seen information from the test data.

Better approach

Complete Dataset
      ↓
Split into train/test
      ↓
Fit preprocessing on training data
      ↓
Apply same transformation to test data

The test set remains isolated during the learning process.

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Training Data vs Testing Data: Why Data Quality Matters

Simply having a large dataset does not guarantee a good machine learning model.

The quality and representativeness of the data matter enormously.

For example, suppose you have:

1,000,000 training records

but almost all of them come from one geographical region.

Your model may perform extremely well on that region but poorly elsewhere.

A good test set should represent the types of data the model is expected to encounter in the real world. Google recommends that test data be statistically meaningful and representative of the dataset and real-world data. (Google for Developers)

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Why Random Splitting Is Common

For many datasets, examples are randomly shuffled before being divided.

Randomization helps make the training and testing subsets more representative of the overall dataset.

For classification problems, stratified splitting can also help preserve class proportions.

For example, imagine a dataset containing:

• 90% non-fraudulent transactions
• 10% fraudulent transactions

A poor split could accidentally create:

Training: 98% non-fraud / 2% fraud
Testing: 58% non-fraud / 42% fraud

That would make evaluation misleading.

Stratified splitting can help maintain a similar class distribution between the subsets. Scikit-learn’s train-test split supports a stratify parameter specifically for this purpose. (Scikit-learn)

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When You Should NOT Randomly Split Data

Random splitting is not always appropriate.

For time-series data, randomly mixing past and future observations can create unrealistic evaluation.

For example, suppose you are predicting:

• Stock prices
• Sales
• Website traffic
• Electricity demand
• Weather
• Customer churn over time

If you train using future information and test on earlier information, your evaluation may not reflect real-world usage.

A time-based approach may be more appropriate:

Older Data                  Newer Data
     ↓                           ↓
Training Data              Testing Data

This better represents a production scenario where a model learns from historical data and then predicts future events.

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How Much Training Data Do You Need?

There is no universal number.

The amount of training data required depends on factors such as:

• Problem complexity
• Number of features
• Model architecture
• Data quality
• Noise level
• Number of classes
• Diversity of examples
• Expected production conditions

A simple model may perform well with relatively little data, while complex deep-learning systems may require significantly more examples.

The goal is not simply to maximize the number of records.

The goal is to provide enough high-quality, representative examples for the model to learn useful patterns.

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What Happens When the Dataset Is Small?

With a small dataset, creating separate training, validation, and test sets can significantly reduce the amount of data available for training.

For example, with only 1,000 records:

700 Training
150 Validation
150 Testing

Only 70% of the available examples are being used directly for model training.

In such situations, cross-validation can be useful.

Scikit-learn explains that k-fold cross-validation repeatedly divides the training data into different folds, allowing different portions to act as validation data while other portions are used for training. (Scikit-learn)

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What Is Cross-Validation?

In k-fold cross-validation, the training data is divided into k subsets called folds.

For example, with 5-fold cross-validation:

Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5

The model is trained and evaluated multiple times.

Round 1

Train: Fold 2 + 3 + 4 + 5
Validate: Fold 1

Round 2

Train: Fold 1 + 3 + 4 + 5
Validate: Fold 2

This continues until each fold has been used as the validation fold.

The resulting scores can then be averaged.

Cross-validation can provide a more robust estimate when the available training data is limited. (Scikit-learn)

Importantly, a separate final test set can still be retained for the final unbiased evaluation.

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Training Data and Testing Data in Deep Learning

The same principles apply to deep-learning systems.

For example, an image classification model may use:

100,000 Images
       |
       +----------------------------+
       |             |              |
    Training      Validation       Test
     70,000         15,000        15,000

The model learns visual patterns from the training images.

The validation images help developers make decisions during development.

The test images provide the final evaluation.

Google’s machine-learning guidance similarly recommends separate training, validation, and testing datasets and emphasizes that the test data should represent the data the model is expected to encounter. (Google for Developers)

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Common Mistakes When Splitting Data

1. Testing on training data

This can produce misleadingly high performance.

Solution: Keep an independent test set.

2. Using test data during model tuning

Repeatedly changing the model based on test results can compromise the independence of the test set.

Solution: Use validation data or cross-validation for development decisions.

3. Data leakage

Information from the test set accidentally enters training or preprocessing.

Solution: Split first and fit data-dependent preprocessing only on training data. (GitHub)

4. Duplicate records

A duplicate example may appear in both training and testing datasets.

This can make test performance look artificially high.

Google gives duplicate examples in train and test data as a specific reason a model can appear to achieve unrealistically strong performance. (Google for Developers)

5. Unrepresentative test data

A test dataset that does not resemble production data can produce misleading conclusions.

Solution: Design the test set around the actual conditions in which the model will operate.

6. Ignoring class imbalance

If one class dominates the dataset, accuracy alone may not provide a complete picture.

Solution: Consider appropriate metrics and stratified splitting where appropriate.

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Which Metrics Should You Use on Test Data?

The evaluation metric depends on the machine learning problem.

Classification

Common metrics include:

• Accuracy
• Precision
• Recall
• F1-score
• ROC-AUC
• PR-AUC

Regression

Common metrics include:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R²

For example, a fraud-detection model may need to prioritize precision and recall rather than simply maximizing accuracy.

A house-price prediction model may instead focus on MAE or RMSE.

The test set provides the data on which these final metrics can be calculated.

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A Simple Python Example

Using scikit-learn, a basic train-test split can be created using train_test_split(). (Scikit-learn)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.20,
    random_state=42
)

Here:

• X = input features
• y = target variable
• X_train = training features
• X_test = testing features
• y_train = training labels
• y_test = testing labels
• test_size=0.20 = 20% of the data is reserved for testing
• random_state=42 = makes the split reproducible

A typical workflow then looks like:

model.fit(X_train, y_train)

predictions = model.predict(X_test)

The predictions can then be compared with y_test using an appropriate evaluation metric.

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A Practical Example With 1 Million Records

Let’s say an e-commerce company wants to predict whether customers will purchase a product.

It has 1,000,000 historical customer interactions.

A possible split might be:

Dataset	Percentage	Records
Training	70%	700,000
Validation	15%	150,000
Testing	15%	150,000
Total	100%	1,000,000

Training

The model learns from 700,000 interactions.

Validation

Developers compare different approaches using 150,000 interactions.

Testing

After finalizing the model, the remaining 150,000 interactions are used for final evaluation.

If the final test performance is significantly worse than validation performance, the team should investigate whether the model is overfitting to the development process or whether the test distribution differs from the data used during development.

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Best Practices for Training and Testing Data

A reliable machine learning workflow should follow several principles.

1. Keep test data independent

Do not allow test data to influence model training.

2. Split before preprocessing

Data-dependent transformations should be learned from training data only. (GitHub)

3. Make the test set representative

The test dataset should resemble the type of data expected in production. (Google for Developers)

4. Remove duplicates

The same or nearly identical examples should not unintentionally appear across training and testing sets.

5. Consider class distribution

For classification problems, preserve appropriate class proportions where necessary.

6. Use validation data for model development

Don’t repeatedly tune your model against the final test set.

7. Consider cross-validation for smaller datasets

Cross-validation can make better use of limited training data. (Scikit-learn)

8. Match the split strategy to the problem

Random splitting may work well for independent observations, but temporal, grouped, or other structured datasets may require specialized splitting strategies.

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Training Data vs Testing Data: The Easy Way to Remember

You can remember the difference with a simple analogy:

Training data = Practice

Validation data = Mock test

Testing data = Final exam

The model studies the training data.

It is adjusted using validation results.

Then it takes the final test using data it has not previously seen.

This process helps determine whether the model has actually learned useful patterns rather than simply memorizing its training examples.

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Final Takeaway

Training data and testing data serve two fundamentally different purposes in machine learning.

Training data teaches the model.

Testing data evaluates the model.

Keeping them separate is essential for measuring whether a model can generalize to new data. If the same data is used for both training and evaluation, performance can be misleading and may hide problems such as overfitting. (IBM)

For many projects, a three-way structure is useful:

                 Dataset
                    |
       +------------+------------+
       |            |            |
     Train       Validate       Test
      70%          15%          15%
       |             |            |
       ↓             ↓            ↓
     Learn       Improve       Evaluate

There is no universal split percentage. The right strategy depends on the amount and structure of data, the machine learning problem, and how the model will be used in the real world.

Most importantly, the test set should represent the data your model will encounter after deployment while remaining independent from the model-development process. (Google for Developers)

Frequently Asked Questions

Is training data the same as testing data?

No. Training data is used to teach the model, while testing data is reserved for evaluating its performance on unseen examples.

Is 80/20 always the best train-test split?

No. 80/20 is a common choice, but there is no universal ratio. Dataset size, class distribution, problem type, and evaluation requirements should influence the decision.

What is validation data?

Validation data is used during model development to evaluate and tune the model before the final test.

Why shouldn’t I test a model using training data?

Because the model has already seen the training examples. Testing on the same data can produce an overly optimistic estimate of performance.

What is overfitting?

Overfitting happens when a model learns the training data too closely and performs substantially worse on unseen data. (IBM)

What is data leakage?

Data leakage occurs when information that should be unavailable during prediction or training improperly influences the model, often producing overly optimistic evaluation results. (GitHub)

Should I use cross-validation?

Cross-validation can be particularly useful when the available training dataset is relatively small because it allows multiple train-validation evaluations while retaining a separate test set for final assessment. (Scikit-learn)

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Sources: Google for Developers, Google News Initiative, scikit-learn documentation, and IBM Think. (Scikit-learn)