What is Training Data for Machine Learning?

One of the most intriguing technologies on the globe is the machine learning algorithm, which solves problems without requiring precise instructions. To work, machine learning algorithms necessitate a large amount of data. It’s difficult to determine what causes an algorithm to perform poorly when working with millions or even billions of photos or records.

With a faulty data gathering method in place, machine learning may be worthless or even detrimental, regardless of the quantity of data and data science talent available. The problem is that the ideal dataset is unlikely to exist. However, there are a few things that firms can do to ensure that their future data science and machine learning activities provide the best outcomes.

What is a training dataset?

A training dataset is required for neural networks and other artificial intelligence algorithms to act as a baseline for subsequent application and use. This dataset serves as the foundation for the program’s ever-expanding library of data. Before the model can analyze and learn from the training dataset, it must be appropriately labeled.

Why is Dataset Collection Important?

Collecting data allows you to capture a record of past events so that we can use data analysis to find recurring patterns. From those patterns, you build predictive models using machine learning algorithms that look for trends and predict future changes. Predictive models are only as good as the data from which they are built, so good data collection practices are crucial to developing high-performing models.

The data need to be error-free (garbage in, garbage out) and contain relevant information for the task at hand. On average, 80% of the time that team spent in AI or Data Sciences projects is about preparing data. Preparing data includes, but is not limited to:

1. Identify Data required
2. Identify the availability of data, and location of them
3. Profiling the data
4. Source the data
5. Integrating the data
6. Cleanse the data
7. prepare the data for learning

Creating Machine Learning Datasets

Let’s imagine we were training someone to recognize the difference between a cat and a dog. We’d show them thousands of pictures of cats and dogs, all different types and breeds. But how would we test them to ensure all those images had sunk in? If we showed them the images they’d already seen, they might be able to recognize them from memory. So we’d need to show them a new set of images, to prove that they could apply their knowledge to new conditions and give the right answer without assistance.

So we need to create three different datasets when training our machine learning model, for training, validation, and testing.

The Training Data

Naturally, we want the model to be as adaptable as possible by the end of the training, thus the training set should include a diverse mix of photos and records. But keep in mind that the model doesn’t have to be perfect at the end of the training. All we have to do now is keep the margin of error to a bare minimum.

At this point, it’s worth introducing the ‘cost function’, a concept widely used among machine learning developers. The cost function is a measure of the variability between the model’s predictions and the ‘right answer’. This data set is used by machine learning engineers to develop your algorithm and more than 70% of your total data used in the project.

Validation Data

It’s time to start the validation stage once we’re satisfied with our cost function and ready to move on from the training. This is similar to a practice exam in that it exposes the model to fresh and unique data without putting it under any pressure to pass or fail.

Using the validation results, we can make any necessary tweaks to the model, or choose between different versions. A model which is 100% accurate at the training stage but only 50% at validation is less likely to be chosen than one which is 80% accurate at both stages, as this second option is better able to face unusual circumstances. Although we don’t need to give the model as much data at the validation stage as it received during training, all the data has to be fresh. If we recycle images the model has been trained with, it defeats the whole object.

Testing Data

We hear you asking, “Why do we need a third stage?” Isn’t the validation stage itself a sufficient test? If the validation stage is long and thorough enough, the model may end up overfitting the data. It might be able to figure out the answer to every query. As a result, we’ll require a third data set with the sole purpose of defining the model’s performance once and for all. We might as well start over if we get a negative outcome on this set.

Again, the test set must be completely fresh, with no repetition from the validation set or the original training set. There are no specific rules on how to divide up your three machine learning datasets. Unsurprisingly, though, the majority of data is usually used for training between 80 and 95%. Ultimately, however, it’s up to each individual team to find their own ratio by trial and error.

TagX your trusted partner

TagX data collection and annotation in Artificial intelligence/machine learning is not only about how data are collected, the quality of the data is also important. There are many factors in Data Quality

1. Data quality requirements
2. Data rules
3. Data policies

If you think there is an opportunity in your organization to take advantage of TagX Data collection and annotation for AI/ML training, explore it and apply it.