A Guide to Data Splitting in Machine Learning

Building a machine learning-based project usually starts from data and ends in a data-driven decision. In between these two points, it includes various sub-steps, some are compulsorily required, and some we use to enhance performance. Data splitting is one of those sub-steps that are not only required, but if followed appropriately, we can get fruitful results from our model. So in this article, we will discuss the basic concepts of data splitting in machine learning.

Let’s start by knowing what data splitting is.

What is Data Splitting?

In data science or machine learning, data splitting comes into the picture when the given data is divided into two or more subsets so that a model can get trained, tested and evaluated.

In practice or in real-life projects, data splitting is an important aspect, and it becomes a must when models are based on the data as it ensures the making of machine learning models. Usually, we create two or three parts of the main dataset.

• If two splits are there, it means one will be utilised for training and another one will be used for testing, or,
• If three splits are there will mean there are training, testing and validation sets.

Let’s say that we have a data set where the data is categorised based on males and females.

We know only two features for each person on the data: weight and voice pitch.

Before modelling this data, we perform a data split procedure to identify the best classification model for classifying the given data value as a male or a female.

How does Data Splitting work?

While performing supervised machine learning tasks, it is always recommended to split the data into three sets: training set, testing set and validation set. So, In the procedure when it comes to data split, first, we randomly split data into three sets:

• Training set: A subset of the main dataset will feed into the model so that that model can learn the data patterns.
• Validation Set: This set is used to understand the performance of the model in comparison to different models and hyperparameter choices.
• Test set: This set checks the final model’s accuracy.

Let’s take a look at the details of these subsets of data.

Train data

A subset of data is responsible for training the model. Usually machine learning model learns to predict by understanding the patterns and relationships hidden inside the data. The model will learn from the patterns and relationships between weight and pitch variables in our example.

While taking train data from whole data, one should take higher representativeness of data into the consideration. This means the extracted data should have enough population for every class of the data. With this quality, one should also ensure that extracted data is unbiased because biased data can lead to an inaccurate model.

the above example represents a problem of classifying data into the male and female classes as a binary classification task. To resolve this problem, we can use a simple decision tree model.

A decision tree will learn by splitting the data into nodes, using the selected feature ( None, Weight, Pitch of voice or both Weight and Pitch of voice.

Validation data

When building a machine learning model, we mostly try to train more than one model by changing model parameters or using different algorithms. For example, while building the decision tree model for our data, we did hyperparameter tuning and found that multiple models performed well in such conditions. Therefore, we need to choose a final model using different parameters.

It has been seen that if we use the same data for training and tuning of a model, tr represents over-fitness and becomes incapable of generalisation.

Here validation set from the data comes into the picture and works as independent and unbiased data, which also helps in the performance comparison of different models.

As this data helps choose the best model algorithm or parameter, we take the model into production after approximating the model’s performance. It is suggested not to use the test data to evaluate the model before selecting the optimum one.

Test Data

As discussed in the above topic, after training, validating and selecting a model, we should take it to production after testing its performance for this extracted subset of data is called the test data.

We should be very careful with this step because if performed ahead of time can form overfitting and lead to unreliable performance. The test set should be used as the final form of evaluation when the use of the validation set is completed and the final model is selected.

Final words

In this article, we have discussed data splitting in machine learning using the points’ what is data splitting’, how does it work, and what is train, test and validation set. In summary of the article, we can have the following takeaways:

• Data splitting becomes a necessary step to be followed in machine learning modelling because it helps right from training to the evaluation of the model.
• We should divide our whole dataset into three sub-dataset.
• The quantity of training data should be higher than the other two data. Also, it should be unbiased to any class or category, so that model can adequately learn from the data.
• We should use the validation set for evaluating multiple models to find the best-performing model.
• After finding the best-performing model, we use the test set to quantify the model’s performance.

Data splitting is a simple sub-step in machine learning modelling or data modelling, using which we can have a realistic understanding of model performance. Also, it helps the model to generalize well to unknown or unseen data.

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