Data Split in Machine Learning – Best Practices & Common Mistakes

Published: May 20, 2025

Ever wondered what is data Split? Well, before any artificial Intelligence (AI) or machine learning model can make smart decisions—like recommending your favorite movie or predicting tomorrow’s weather—it needs to learn from past data.

But here’s the catch: you can’t let it see everything at once.

That’s where data splitting comes in.

This guide will help you:

Understand what data split really means
Learn how to split your data the smart way
Avoid common mistakes beginners make
And even write your own data split code (in Python and R!)

Whether you’re just starting with machine learning or looking to improve your model accuracy, mastering splits is a skill you can’t skip.

Let’s break it down!

Table of Content

What Does Data Splitting Mean?
Difference Between Training Data and Testing Data
Why Do We Split Datasets?
Example Use Case: Train and Test Data in Machine Learning
Common Types of Data Splits
How to Split Data into Training and Testing (with Code Examples)
Splitting Data in Python
Advanced Techniques & Considerations
Common Mistakes in Data Splitting
Mastering Splits - Best Practices
1. Do’s
2. Don’ts
FAQs
Final Thoughts
Bonus Info Points on Data Split

What Does Data Splitting Mean?

Data Splitting in machine learning simply means dividing your data into parts.

Instead of using your full dataset to train your model, you split it into chunks so that your model can learn properly and be tested fairly.

Why split at all?

Imagine you’re preparing for a big quiz.
If you memorize all the questions and answers, sure—you’ll ace the practice.
But when the real exam throws in a new question, you’re stuck.

That’s exactly what happens if we train a model on all the data—it memorizes instead of learning patterns.

By splitting the data:

We let the model train on one part (training set)

And test it on another part it’s never seen (test set)

This shows us how well the model can actually perform on new, real-world data.

A Simple Analogy

Think of a chef testing a new recipe:

They practice with some ingredients (training)
Taste it midway and adjust (validation)
Serve it to guests for real feedback (testing)

Would you want to be the guest eating a dish the chef never tasted beforehand? Probably not!

Common Terms You’ll Hear

Training Data: The part your model learns from. It finds patterns, relationships, and builds its “brain.”
Test Data: The part your model is judged on. It checks how well the learning worked.
Validation Data (optional but useful): A middleman to fine-tune the model before final testing.

These sets are usually split in ratios like 80/20, 70/30, or 60/20/20 depending on your data and project.

Splitting is all about preparing your model to perform well—not just on paper, but in the real world.

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Difference Between Training Data and Testing Data

Now that we know what splitting is, let’s talk about the two most important parts:
training data and testing data.

You’ll hear terms like training and test data, training test data, or even training and testing data thrown around a lot — and while they sound similar, they serve very different jobs in machine learning.

What is Training Data?

Training data is like a classroom.

This is the data your model studies from. It looks at examples, learns patterns, builds logic, and tries to understand how things work.

For example, if you’re training a model to recognize cats and dogs, the training data will show it hundreds (or thousands!) of labeled images of cats and dogs.

The model learns the difference by analyzing this data over and over.

What is Testing Data?

Testing data is like the final exam.

It’s brand new to the model — something it hasn’t seen before. This is where we check:

“Hey, did you actually learn something, or did you just memorize your training set?”

Testing data helps us measure how well the model performs in real-life situations. It’s all about accuracy, performance, and generalization.

Why the Difference Matters

If we don’t separate training and testing data properly, things can go very wrong:

The model might overfit — meaning it’s great at the training data but awful on new data.
It gives a false sense of accuracy — like scoring 100% on a test you already saw the answers to.
It can’t be trusted in real applications — which defeats the whole purpose!

This is why it’s important to always split data into training and testing sets.

Whether you call it train and test data split, training test split, or training testing data separation, the goal is the same:
Build a model that actually works beyond the practice set.

Why Do We Split Datasets?

By now, you’ve probably heard a lot about splitting data into training and testing sets. But what’s the real reason behind it? Why is this step so important in machine learning?

Let’s break it down in a straightforward way.

Purpose of Splitting Data

The main reasons we split a dataset are:

To avoid overfitting
To improve how we evaluate the model

Both of these are essential if you want to build a model that actually works in the real world.

Avoiding Overfitting

Overfitting happens when a model memorizes the training data too well.

It becomes so good at remembering the examples it saw during training that it performs poorly on new, unseen data.

By splitting the dataset into training and test data, we give the model one part to learn from and another to be tested on.

This way, we can check if the model is just memorizing or actually learning patterns that can be applied to new situations.

This is especially important in machine learning projects, where the goal is to create a model that works with real-world data, not just the examples it was trained on.

Better Model Evaluation

After training, we want to see how well the model performs. We use the testing data to do this. Since the model hasn’t seen this data before, it gives us a more honest view of its accuracy.

A proper training test data split helps us understand whether the model is reliable and ready to be used for real predictions.

Example Use Case: Train and Test Data in Machine Learning

Suppose you are building a machine learning model to predict house prices. You would start with a dataset containing information like the size of the house, location, and price.

The training dataset would include most of these records. The model uses this data to learn how different factors affect house prices.

The testing dataset includes the remaining records. These are used to check how well the model predicts prices it hasn’t seen before.

This process helps ensure the model is both accurate and trustworthy.

Splitting the dataset into training and testing sets is a basic but crucial step in building machine learning models.

It helps avoid overfitting and gives you a fair way to measure performance.

Whether you call it splitting data, training test data, or training and testing data, the idea is simple—teach your model with one part, and test it with another to make sure it’s truly learning.

Common Types of Data Splits

When it comes to splitting data, there are a few popular ways people usually go about it. Let’s look at the most common types of data splits you’ll encounter.

Train/Test Split: The Classic Approach

This is the simplest and most common method. You split your dataset into two parts: one for training the model, and one for testing it.

You might hear this called train test split, training test split, or even test train split—but they all mean the same thing.

Usually, the split is around 70% training data and 30% testing data, or 80/20 depending on how much data you have.

The model learns from the training set and then gets evaluated on the testing set.

This approach works well when you have a good amount of data and want a quick way to check model performance.

Train/Validation/Test Split

Sometimes, just splitting data into training and testing sets isn’t enough. That’s where the training validation test split comes in. Instead of two parts, you split your data into three:

Training set: The model learns from this.
Validation set: Used to tune the model and make decisions, like adjusting settings.
Testing set: The final check to see how well the model performs on unseen data.

Common ratios for this kind of split are:

60% training / 20% validation / 20% testing
70% training / 15% validation / 15% testing

This approach gives you a more balanced and thorough way to train and evaluate your model.

K-Fold Cross Validation and Stratified Splitting

Another popular technique is K-Fold Cross Validation. Instead of splitting the data just once, you divide it into K equal parts (folds).

The model trains on K-1 parts and tests on the remaining part. This process repeats K times, each time with a different fold as the test set.

This method is great for getting a better understanding of how your model performs across different data splits, especially when you don’t have a lot of data.

Stratified splitting is a variation where the splits keep the original class proportions intact.

This is useful when dealing with imbalanced datasets, making sure each split fairly represents all classes.

Splitting Data Three Ways

Splitting data three ways basically means dividing your dataset into training, validation, and testing sets — like we discussed with the train/validation/test split.

This helps you not just train and test, but also fine-tune your model to get better results.

In summary, knowing which type of data split to use depends on your project, the amount of data you have, and how precise you want your model evaluation to be.

Whether it’s a simple train/test split, a three-way split, or using advanced methods like K-Fold cross validation and stratified splitting, each method has its own strengths.

How to Split Data into Training and Testing (with Code Examples)

Now that you know why splitting data is important and the common types of splits, let’s see how to actually do it!

Below, I’ll show you easy examples in both Python and R — two of the most popular languages for data work.

Splitting Data in Python

Python makes splitting data super simple, especially with the help of the scikit-learn library.

Here’s a quick example using the train_test_split function to split your dataset into training and testing sets:

Install and load caTools package

install.packages(“caTools”)
library(caTools)

Example dataset

data <- data.frame(x = 1:10, y = c(0,1,0,1,0,1,0,1,0,1))

Set seed for reproducibility

set.seed(42)

Split data: 70% train, 30% test

split <- sample.split(data$y, SplitRatio = 0.7)

train_data <- subset(data, split == TRUE)
test_data <- subset(data, split == FALSE)

print(“Training data:”)
print(train_data)
print(“Testing data:”)
print(test_data)

random_state=42 ensures you get the same split every time you run the code.

This is the most common way to split dataset into train and test in Python.

Splitting Data in R

If you work with R, splitting datasets is also straightforward. One popular approach is using the caTools package. Here’s how you can do it:

Advanced Techniques & Considerations

Once you’ve got the basics of splitting data down, there are some extra things to think about—especially when your data or project has special needs.

Let’s go over a few advanced tips and tricks.

Dealing with Imbalanced Data – Split Sampling

Sometimes your dataset isn’t balanced. For example, in a fraud detection dataset, the number of fraud cases might be way smaller than the normal cases.

If you just do a random split, your training or testing data might end up with very few fraud examples, which messes up the model.

This is where split sampling or stratified splitting comes in. It makes sure that the proportions of different classes stay the same in both training and testing sets.

This helps your model learn better and evaluate more fairly.

Custom Ratios: 80/20 Split and More

While the classic 70/30 split is common, sometimes you want to customize the ratio based on your needs:

80/20 split: More training data means the model gets more to learn from.
Training validation split: Adding a validation set (like 60/20/20 or 70/15/15) helps tune your model better.
Splitting ratio: Adjust this depending on how much data you have and how complex your model is.

Remember, the right split ratio can vary depending on your project, so don’t be afraid to experiment.

Domain-Specific Strategies

Different types of data sometimes need special ways to split:

NLP (Natural Language Processing): You might want to split based on documents or sentences to avoid data leakage.
Time-series data: Here, you usually split by time—training on older data and testing on newer data to mimic real-life scenarios.
Image data: You often use techniques like split one technologies (tools that help manage large image datasets) or specific augmentations along with splits.

These domain-specific strategies help you get the best out of your data and models.

What Can Go Wrong? Splits Breaking & Fails

Not all splits go smoothly. Sometimes, splits fail because of issues like:

Data leakage (where info from test data sneaks into training)
Poor sampling (which makes your training or test set unrepresentative)
Splits breaking the natural structure of data (like shuffling time-series randomly)
Doing a good split analysis after you create your splits can catch these problems early.

Advanced splitting isn’t just about dividing data—it’s about smartly handling your unique dataset and avoiding common pitfalls.

Whether you’re balancing classes, customizing ratios, or using domain-specific methods, these extra steps help make your models stronger and more reliable.

Common Mistakes in Data Splitting

Even though splitting data sounds simple, it’s easy to make mistakes that mess up your model’s performance. Let’s look at some of the most common pitfalls to watch out for.

Data Leakage

This is probably the biggest mistake you can make. Data leakage happens when information from your test set accidentally gets into your training data.

If this happens, your model ends up “cheating” because it already knows stuff it’s supposed to learn or predict.

For example, if you shuffle your data incorrectly or use future data in training, your model will look great on paper but fail in the real world.

Always make sure your training and testing sets are completely separate.

Shuffling Issues

Sometimes people shuffle data without thinking about the structure behind it.

Shuffling is usually good because it mixes up your data randomly, but with some types of data—like time-series or grouped data—shuffling can break important order or relationships.

If you shuffle time-series data, you might train your model on future information, which isn’t realistic. So, be careful when shuffling and understand your data first.

Wrong Validation Methods

Not using the right validation method can lead to wrong conclusions about your model’s accuracy. For example:

Using a simple train/test split when you actually need cross-validation to get a better estimate.
Skipping a validation set entirely, which means no chance to tune your model properly.
Applying random splits in data where stratified splits are needed, like with imbalanced classes.
Choosing the wrong validation method can waste time and give you misleading results.

Avoiding these common mistakes in data splitting is key to building solid models that work well outside your dataset.

Keep an eye on data leakage, understand when and how to shuffle, and pick the right validation approach for your project.

Mastering Splits – Best Practices

To get really good at mastering splits, you need to follow some smart do’s and don’ts.

Here are some easy-to-follow tips, plus useful splitting examples and pointers on how to split data into training and testing the right way.

Do’s

Do’s
Do understand your data first. Know if it’s time-series, imbalanced, or grouped before splitting. Do use stratified splits when working with imbalanced classes to keep proportions consistent. Do set a random seed to make your splits reproducible—this helps if you want to share your work or debug later. Do split your data before any preprocessing to avoid data leakage. Do try different split ratios (like 70/30 or 80/20) depending on your dataset size and model needs. Do use cross-validation when possible to get more reliable model performance estimates. Do analyze your splits—check if training and test sets look representative of the overall data.

Do understand your data first. Know if it’s time-series, imbalanced, or grouped before splitting.
Do use stratified splits when working with imbalanced classes to keep proportions consistent.
Do set a random seed to make your splits reproducible—this helps if you want to share your work or debug later.
Do split your data before any preprocessing to avoid data leakage.
Do try different split ratios (like 70/30 or 80/20) depending on your dataset size and model needs.
Do use cross-validation when possible to get more reliable model performance estimates.
Do analyze your splits—check if training and test sets look representative of the overall data.

Don’ts

Don’ts
Don’t mix training and testing data. Keep them separate to avoid data leakage. Don’t shuffle data blindly. Be careful with time-series or grouped data where order matters. Don’t skip the validation set if you need to tune hyperparameters. Don’t rely on just one split. Try multiple splits or cross-validation to avoid biased results. Don’t forget to check your splits—sometimes splits fail or break the natural structure of your data.

Don’t mix training and testing data. Keep them separate to avoid data leakage.
Don’t shuffle data blindly. Be careful with time-series or grouped data where order matters.
Don’t skip the validation set if you need to tune hyperparameters.
Don’t rely on just one split. Try multiple splits or cross-validation to avoid biased results.
Don’t forget to check your splits—sometimes splits fail or break the natural structure of your data.

When you’re learning how to split data into training and testing, remember: start simple with a 70/30 train test split, make sure you keep things separate, and adjust based on your project’s needs.

Over time, you’ll build confidence with more advanced techniques and splitting examples.

FAQs

What is data splitting in machine learning?

Data splitting is the process of dividing a dataset into separate parts, usually training and testing sets. This helps to train a machine learning model on one part and evaluate its performance on unseen data. It ensures the model can generalize well to new, real-world data.

Why do we split datasets into training and testing sets?

We split datasets to prevent overfitting, where a model performs well on training data but poorly on new data. Training data helps the model learn patterns, while testing data checks how well the model predicts on data it hasn’t seen before. This split gives a realistic evaluation of the model’s accuracy.

What is the difference between training data and testing data?

Training data is the portion of the dataset used to teach the model, allowing it to learn patterns and relationships. Testing data, on the other hand, is kept separate to assess the model’s performance on unseen information. This distinction helps ensure the model is reliable and not just memorizing the training data.

How do you split data into training and testing in Python?

In Python, you can use libraries like scikit-learn, which offers the train_test_split function to easily divide data. You provide your dataset and specify the ratio (like 80/20), and it returns separate training and testing sets. This method is quick and reliable for most machine learning projects.

What is a typical training-validation-test split ratio?

Common ratios are 70/15/15 or 60/20/20, meaning 70% or 60% for training, and the remaining split between validation and testing. Validation data is used to tune model parameters without touching the test set. This helps improve model performance while keeping the test set truly independent.

What is cross-validation and how does it relate to data splitting?

Cross-validation is a technique where data is split into multiple parts (folds), and the model is trained and tested multiple times using different folds. It provides a more robust estimate of model performance compared to a single train-test split. This approach reduces the risk of random bias in how the data is split.

How do you handle imbalanced datasets during splitting?

For imbalanced datasets, it’s important to keep the class distribution similar in both training and testing sets. Techniques like stratified splitting ensure that each subset has the same proportion of classes. This helps the model learn and be evaluated fairly across all categories.

What are common mistakes to avoid when splitting data?

Avoid data leakage, where information from the test set accidentally influences training, as it can give overly optimistic results. Not shuffling data before splitting can cause biased splits, especially with ordered data. Also, using the wrong validation method may lead to poor model evaluation.

How does splitting data affect model performance?

The way data is split directly impacts how well your model generalizes to new data. A poor split can cause overfitting or underfitting, leading to inaccurate predictions. Proper splitting ensures the model is tested fairly and performs well outside the training set.

Can data splitting be automated, and what tools help with it?

Yes, data splitting can be automated using libraries like scikit-learn in Python or caret in R, which provide built-in functions for splitting datasets. These tools allow you to customize split ratios, perform stratified splits, and handle cross-validation easily. Automation saves time and reduces errors in preparing your data.

Final Thoughts

Understanding data split is a must if you’re working with machine learning or data science.

It might sound technical at first, but it’s really just about making sure your model learns from one part of the data and is tested on another to see how well it performs.

Whether you’re using Python, R, or any other tool, knowing how to split your data the right way helps you build smarter, more reliable models.

By mastering different types of data splitting—like train/test, validation sets, or cross-validation—you set your project up for success.

So next time you start a machine learning project, don’t skip the split. It’s a small step that makes a big difference.

Bonus Info Points on Data Split

80/20 Rule is Popular: A common rule of thumb is to use 80% of your data for training and 20% for testing. It’s not a hard rule but works well in many cases.
Always Shuffle Before Splitting: Shuffling your data randomly before splitting helps avoid bias, especially if your data is ordered in any way.
Use Stratified Splits for Classification: If you’re dealing with classification problems, stratified splitting ensures that both training and testing sets have similar class distributions.
Validation Sets Help Tune Models: Don’t rely on test data to tweak your model. Use a validation set or cross-validation for that.
Time-Series Data Needs Special Splits: For time-based data, avoid random splits. Use methods that respect the time order to prevent data leakage.
Small Datasets? Try Cross-Validation: If you don’t have much data, cross-validation is better than a single split—it uses all your data more effectively.
Split Ratios Are Flexible: You don’t always need to go with 80/20. Depending on your dataset size, you might use 70/30, 60/20/20, or even 90/10.
Avoid Data Leakage: Make sure no data from your test set “leaks” into your training set—this gives you misleading results.
Automation Tools Help: Use tools like train_test_split in Python or createDataPartition() in R to make splitting faster and error-free.
Reproduce Your Results: Set a random seed (e.g., random_state=42) when splitting data to ensure you get the same results every time.

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