So, What Are Information Sets in ML?
Machine Learning is all about feeding the computer information so it can make smart guesses. But we can’t just throw all the data in at once.
We break the data into different chunks—called information sets—and each one plays a special role in helping the machine learn, practice, and improve.
The 3 Main Sets That Teach the Machine
Think of machine learning like school. The computer is the student. And the data is the study material.
Let’s break it down:
1. Training Set – Where It All Starts
This is the machine’s main study guide.
We feed it examples of things we want it to learn. For example, if we’re teaching it to tell cats and dogs apart, we show it hundreds of pictures and tell it which ones are cats and which ones are dogs.
The machine looks for patterns—like “cats usually have pointy ears” or “dogs have longer snouts.” Over time, it starts to figure things out.
Real-life example:
Photos of animals labeled “cat” or “dog.” The machine studies them to learn the difference.
2. Validation Set – Time for a Check-in
This is like a mini quiz.
After the machine has been trained a bit, we give it some new examples (not from the training set) to see how well it’s doing.
If it’s making mistakes, we adjust the settings—like changing how deep it thinks or how many features it looks at. This step helps tune the machine without cheating.
Real-life example:
New animal photos that the model hasn’t seen before, just to see if it’s catching on.
3. Test Set – The Final Exam
This is the big one.
Once the machine seems ready, we give it completely new data—data it has never seen. This is the test to see if it can apply what it’s learned in the real world.
If it passes, it means the model is ready to be used in apps, websites, or whatever it was built for.
Real-life example:
Final batch of pet photos, and we check if the machine guesses correctly without help.
A Simple Comparison
Let’s say you’re teaching a kid how to do math:
- Training set = practice worksheets
- Validation set = pop quiz
- Test set = school exam
Same idea with machines.
Visual Summary
Real-World Example: Spam Email Filters
Let’s say you’re making a spam filter for email. Here’s how these sets work:
- Training set: Emails already marked as “spam” or “not spam”
- Validation set: New emails used to fine-tune the model
- Test set: Brand-new emails to test if the model works well
Each set helps at a different stage—and all three are needed.
Common Mistakes to Watch Out For
Even in simple projects, beginners often make these mistakes:
- Using the same data in all sets – this gives a fake “perfect” score
- Skipping the validation set – you won’t know if the machine is truly learning
- Imbalanced data – if you train on mostly one thing (like 90% cat pics), your model gets biased
Bonus: Other Sets You Might Hear About
Sometimes people use other terms, especially in big projects:
- Holdout Set – like a backup test set for later
- Cross-validation – a method that swaps data around to test the model better
- Augmented Set – added data made by flipping images, adding noise, or changing brightness
You don’t need these at the start, but they help in complex tasks.
Tips to Build Better Data Sets
If you’re preparing your own machine learning project, here are a few quick tips:
- Keep each set separate. No mixing!
- Make sure your examples are balanced.
- Label clearly—the machine only knows what you teach it.
- Test with real-life examples—what works in training might break in the wild.
Recap in 30 Seconds
- You need three info sets: training, validation, and test.
- Each one helps the model learn in a different way.
- If you skip one, your results could be misleading.
- Just like studying in school, the learning happens in steps.
Frequently Asked Questions (FAQ)
1. What are information sets in machine learning?
Information sets are groups of data used to teach a machine learning model. They’re organized into three main types: training, validation, and test sets. Each one plays a different role in helping the machine learn and improve.
2. Why do we need different sets like training, validation, and test?
Each set serves a specific purpose:
- Training Set: Teaches the model by showing labeled examples.
- Validation Set: Helps fine-tune the model and adjust settings.
- Test Set: Checks if the model works well on brand-new data.
Without these separate sets, the machine might just memorize instead of learning properly.
3. Can I use the same data for all three sets?
No, and you shouldn’t. Using the same data can lead to overfitting, where the model performs well during training but fails in real-world situations. Keep each set separate and unique to get reliable results.
4. How do I split my data into these sets?
A common rule is:
- 60% for training
- 20% for validation
- 20% for testing
But you can adjust this depending on the size of your dataset. The goal is to give the model enough examples to learn, tune, and test.
5. What happens if I skip the validation set?
Without a validation set, you won’t know how to adjust your model properly. You might end up with a model that looks great during training but performs poorly when faced with new data.
6. Is the test set the most important one?
Not really—they’re all important in different ways. The training set builds the model, the validation set improves it, and the test set tells you if it works. Skipping any one of them can lead to weak or misleading results.
7. How can I tell if my model is overfitting?
If your model scores really high on the training set but low on the validation or test set, it’s likely overfitting. That means it memorized examples instead of learning patterns. You can fix this by adjusting the model or giving it more data.
8. What’s a real-world example of these sets?
Let’s say you’re building a model to recognize spam emails:
- Training Set: Emails already marked spam or not spam
- Validation Set: New emails to tweak the spam filter
- Test Set: Completely unseen emails to check how well the model works
9. Are there more than three types of sets?
Yes. In bigger or more complex projects, you might hear about:
- Holdout sets (extra test data saved for later)
- Cross-validation sets (data that rotates between train/test roles)
- Augmented sets (data created using tricks like flipping images)
But to start, the basic three are enough.
10. Do I need a lot of data to use all three sets?
Not always. Even with a small dataset, you can use simple splits or cross-validation to make the most of your data. The key is to avoid overlap between sets and keep them balanced.