More and more businesses - regardless of size, budget, and industry - are increasingly embracing digital technologies such as artificial intelligence (AI) and machine learning as well as Open Legacy cloud applications architecture.

But, for a company to truly make the most out of these incredibly valuable tools, it's paramount that it becomes familiar with what they are, what they do, and how they do it.

Let's take machine learning, for example. The use of this technology is becoming so pervasive that it's almost impossible that your business is currently not using at least one machine learning tool.

Whether it's speech recognition, predictive analytics, or data extraction, the chances are that you, too, leverage ML algorithms on a daily basis. Are we right?

If so, then our guide right here is essential. Keep reading to find out more about what train, test, and validation are, and why you need them for your machine learning tools.

Getting familiar with train, test, and validation

When it comes to machine learning, few things are as essential as training, testing, and validation sets (except, of course, data protection).

Let's discover what each of these concepts means, and why they are all so crucial.

Training set

Essentially, machine learning is all about data. In the case of the "train" set, data is used to - you guessed it - train the model and enable it to learn all the subtle, sometimes hidden, features and patterns within it.

To gain as much as possible from this phase, you should keep feeding the same training data to the neural network architecture, as this helps the model understand more about the data itself.

Testing set

When you hear the word "test" in the context of data, you might immediately think about Test.io or a Test.io alternative like Global App Testing. Well, in this case, it's a little bit different.

After your training phase, you want to introduce a different set of data for your testing stage. With this data, you will be able to uncover a series of accurate and unbiased metrics that let you figure out how well your model is performing.

Validation set

The validation phase is the last part of your process. Here, you will need to use a third set of data - separate from the previous two - that helps you validate the way your model performed during training.

Depending on your findings, you will be able to better adjust the configurations and parameters of your models, and decide whether or not you are achieving what you had set out to.

Train, test, and validation split in machine learning

Now that we have explained what the train, test, and validation phases involve, let's take a step forward and try to understand why their split is so vital in a machine learning setting.

First of all, by having three different datasets to work from (and with) - one for each phase - you can get a better, more accurate, and more up-to-date view of how your model is performing in practice.

Ultimately, your model is what allows your machine learning tool to keep improving and performing at its best, thus supporting your business across a range of areas.

Secondly, splitting the datasets is also essential because it can help you pinpoint where things are not going as you would like them to. As a result, it enables you to take action quickly and meaningfully.

In terms of the split ratio for your three datasets, it's important to note that there are no set ‘rules'. Rather, it very much depends on your company's needs and goals.

Generally speaking, though, you want to consider the number of samples in each dataset as well as the model that you are working with. For example, your training data should be large enough to allow for an accurate and realistic validation phase.

To simplify, you might want to consider allocating around 80% of your data in the training phase, 10% in the validation phase, and the remaining 10% in the test phase.

This first type of split can be a good start, but remember to keep reviewing and tweaking it according to factors such as your model structure, data size, use case, and so on.

The takeaway

Cloud-based technologies such as artificial intelligence (AI) and machine learning will continue to be prominent staples in the digital world for years to come.

Therefore, if you want to make the most out of them - and truly reap their business potential - it's imperative to be as familiar with them as you possibly can.

Hopefully, our guide has given you a clearer understanding of the train, test, and validation split, and why you should apply these sets to your machine learning tools.

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ABOUT THE AUTHOR

Emily Rollwitz - Content Marketing Executive, Global App Testing

Emily Rollwitz is a Content Marketing Executive at Global App Testing, usability testing and QA service company helping top app teams deliver high-quality software anywhere in the world. She has 5 years of experience as a marketer, spearheading lead generation campaigns and events that propel top-notch brand performance. Handling marketing of various brands, Emily has also developed a great pulse in creating fresh and engaging content. She's written for great websites like Airdroid and Agility PR Solutions. You can find her on LinkedIn.