Training Data vs Test Data vs Validation Data

We often get asked about the difference between training data vs test data in machine learning. 

Knowing the difference and ensuring you're using both the right way is essential. In this article, we will discuss training data vs test data vs validation data and explain more about each.

It aims to be an introduction for anyone who needs to know the difference between the various dataset splits while training Machine Learning models.

Machine learning (ML) is a branch of artificial intelligence (AI) that uses data and algorithms to mimic real-world situations so organizations can forecast, analyze, and study human behaviors and events.

ML usage lets organizations understand customer behaviors, spot process- and operation-related patterns, and forecast trends and developments. Many companies, in fact, have made ML an integral part of how they operate.

Constructing ML algorithms depends on how they will collect data. And more often than not, the information gathered is categorized into three types. 

The machine learning process uses three data sets in creating algorithms:

• training data, 
• validation data, 
• and test data. 

Let us distinguish one from the others.

What is the Training Dataset in Machine Learning?

Training data is used to train a model to predict an expected outcome.

An outcome based on the result of regression or classification problems, for example, churn prediction, sales lead scoring prediction, or timeseries forecast. 

The algorithm's design thus focuses on the outcome of the expected or predicted result.

Training data is the actual dataset we use to train the model. We can say that the model sees and learns from this data.

Training data teaches an algorithm to extract relevant aspects of the outcome. It is often the initial dataset used to make a program understand how to apply different features, aspects, and technologies to reach the desired outcome.

Let's see how a training dataset example for Predicting Sales Lead Conversion should look like:

The model will use features (columns) to train on the outcome (target variable, "Converted - YES/NO").

Training the model requires running the training data set and comparing the result with the target or expected outcome. Using the comparison as a guide, the model's parameters are adjusted until the desired target is reached.

Validation Dataset

The validation dataset is the data set used to check the accuracy and quality of the model used on the training data. It's meaning is not to teach a model, even if the machine undergoing training sees it. Instead, it only reveals biases so the model can be adjusted to produce unbiased results.

We can say that the validation set affects a model, but only indirectly. Sometimes, the validation set is known as the Development set since this dataset helps during the development stage of the model.

What is a Testing Dataset?

The testing dataset is used to perform a realistic check on an algorithm. It confirms if the ML model is accurate and can be used in the forecast and predictive analyses.

Based on our previous example for Predicting Sales Lead Conversion, we imagine this is how testing dataset should look like:

Since we know exactly what an outcome should be (Converted YES or NO), we can see the model performance and accuracy. Machine learning models count how many times the model correctly predicted the target outcome ("Converted").

Test data is similar to validation data, but unlike the latter used during training, test data is only used once on the final model.

The final model is completely trained using the training and validation data sets.

The test set is generally used to assess competing models, meaning it determines which model provides better results.

Training Data vs Test Data vs Validation Data : Train/Test Split

As you may have already gleaned from the definitions of training, validation, and test data above, teaching an ML model requires splitting your data into two primary datasets—one for training and another for testing.

Data splitting ensures that an algorithm model can help analysts find features or aspects influencing an outcome or result.

Probably the most standard way to go about data splitting is by classifying 

• 80% of the data as the training data set 
• and the remaining 20% will make up the testing data set.

In ML, that means 80% of the entire data set is classified as training data, while the remaining 20% becomes test data. But why 80:20?

Have you ever heard of the Pareto Principle? It is also known as the "80/20 rule," which states that 80% of effects come from 20% of causes. It has been applied throughout time to wealth distribution because, statistically, it does come close to explaining many human, machine, and environmental phenomena. So, analysts have begun applying the rule to ML models as well.

Why do we split the data in machine learning?

If you are wondering why data needs to be split, it is pretty simple—you want to assess the model's performance when its users do not have expected outcomes or results.

Always make sure that your test dataset meets the following conditions:

• It is large enough to yield statistically meaningful results.
• It is representative of the data set as a whole. That means don't pick a test set with different characteristics than the training set.

Best Practices When Creating Training Data

Building a training data set does not merely mean collecting data and then running it through an AI algorithm to see if the model works. It requires analysts to follow certain practices to ensure that the data's circumstances mimic real-world situations. The forecasts or predictions the model provides cannot be trusted if they do not.

Here are some best practices you can follow when creating a training data set for your algorithm.

1. Avoid target leakage and ensure that the training data set only includes data related to the expected outcome. Leakage occurs when a variable used in the model is not a factor in attaining the target result. It happens when the model uses data that is not available or is considered unseen data.
2. Prevent training-serving skew. Ensure no changes are made to the training data, the final testing data set, and the serving pipelines. Skews occur when the data used undergoes changes from when it was used for training to when it was served.
3. Use time signals. If you expect a pattern to shift over time, you need to provide the algorithm with time signal information to adjust to the pattern shift.
4. Include clear information where needed. If your dataset requires explicit explanation, include features that will let the algorithm understand that information clearly. Such data can consist of email addresses, locations, or phone numbers.
5. Avoid bias. Ensure that your training data is representative of the potential data you will use to develop predictions.
6. Provide enough training data. The model's performance may not fit your target output if you do not have a sufficient quantity.

Build Better Machine Learning Algorithms

An algorithm is only as good as the training data it is fed. If it is not trained using enough data, you will end up with unrealistic analyses and predictions.

How Much Training Data do I Need?

Ensure you have at least 1,000 data rows in your training data set.

Alternatively, another rule of thumb is to have at least 20x more rows than columns in your dataset.

You need as much training data as possible with relevant features (columns) to the target outcome. Otherwise, you cannot ensure the model will work as expected if it was trained using small data sets when users exceed the training volume.

Data Quality

The training data must mimic what happens in the real world. It can include CRM data, documents, numbers, images, videos, and transactions with features vital to your target result. 

If that is not the case, then the algorithm's result will not be realistic.

Conclusion

It is essential to have quality training data to perform any machine learning task. You need the right quality and quantity of training data for training your model.

Now that you understand more about training data vs test data vs validation data in machine learning and why it’s important, you can create your own prediction models.

Forecasting requires extensive machine learning and statistics knowledge. Luckily, if you don't have the in-house talent to do the job, there are no-code machine learning solutions like Graphite with ready-to-go prebuilt models. You can run your predictions without writing a single line of code.

Products like Graphite make it possible for any business-savvy individual to understand their options more straightforward and more user-friendly.