How To Generate Quality Training Data For ML Models

Generate Quality Training Data For ML Models

When training data for machine learning (ML) models, you have to consider quality more than quantity. In fact, having a large amount of low-quality training data can hurt your model’s performance. On the other hand, a small amount of high-quality training data can lead to much better results.

What is quality training data?

Quality training data is a set of accurate data representative of the real-world conditions your model will be used in, free of any bias, and labeled correctly. (1)

The ML models are only as good as the data they’re trained on. If your training data is of poor quality, your model will be of poor quality. Quality training data is essential for building accurate and reliable machine learning models.

So, to ensure you generate quality training data, here are eight helpful tips:

1. Make representative data

One of the most important things to consider when creating training data is whether or not it is representative of the real-world data that your model will be used on. If your training data is not representative, your model will likely perform poorly in the real world.

To create representative training data, start by understanding what kind of data your model will be used on. For example, if you’re building an ML model to classify images of animals, you’ll need to make sure that your training data contains images of all the different kinds of animals that your model will need to learn to identify.

2. Make sure your data is labeled correctly

Another critical thing to consider when creating training data is whether or not the data is labeled correctly. Incorrect labels can lead to poor performance of your ML model.

To label data correctly, you’ll need to understand the task that your model is being trained to perform. It’s also best to use a data labelling platform or tool that can help you label data accurately. This way, you can avoid any human error.

3. Make sure your data is free of bias

Bias can be a significant problem in ML. If your training data is biased, your model is likely inaccurate. There are different types of bias in ML, including exclusion, sampling, observer, measurement, recall, association, and racial bias, leading to inaccurate results. (2)

To avoid bias in your training data, you need to be aware of the different types of bias and how they can affect your data. You also need to select a random sample of data when creating your training set.

4. Make sure your data is free of noise

Noise (syntactic) is another common problem in ML. There are many different types of noise in ML, but some of the most common are outliers, missing values, and incorrect values. (3)

To avoid noise in your training data, you need to be aware of the different types of noise and how they can affect your data. You also need to clean your data before using it to train your model.

5. Balance your data

If your training data is unbalanced, your model is likely to be inaccurate. An unbalanced dataset is one where the classes are not equally represented. For example, if you’re training an ML model to classify images of animals, and your dataset only contains images of dogs, your model is likely to be less accurate than if your dataset contained a balanced mix of animal images.

To overcome the issues when training an unbalanced dataset, you can use methods such as upweighting and downsampling. Upweighting is when you increase the weight of the minority class, and downsampling is when you decrease the size of the majority class.

Both upweighting and downsampling can be used to train an unbalanced dataset. However, you need to be careful not to overfit your data when using these methods.

6. Split your data into training and test sets

Once you’ve created the dataset, it’s important to split it into training and test sets. You’ll use the training set to train your model, while the test set is for you to evaluate your model’s performance.

It’s essential to ensure that the training and test sets represent the data that your model will be used on. For example, if you’re building an ML model to classify images of animals, you’ll need to secure your training and test sets, both containing images of all the different kinds of animals that your model will need to be able to identify.

7. Preprocess your data

Preprocessing is a crucial step in preparing data for machine learning. Preprocessing can help improve your ML model’s performance by making the data more amenable to learning.

There are many different types of preprocessing, but some of the most common are feature scaling, normalization, and one-hot encoding, which can all be used to improve the performance of your machine learning model. (4)

8. Augment your data

Lastly, you may also want to augment your data. Data augmentation is a technique used to artificially increase the size of your dataset by creating new, synthetic data points from existing data points.

Data augmentation can be used to improve the performance of your ML model by making it more resistant to overfitting. There are many different ways to augment data, but the most common are adding noise, randomly rotating or flipping images, and randomly cropping images. (5)

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Final Thoughts

Training a machine learning model can be a time-consuming and challenging process. However, by following the tips in this article, you can make the process easier and improve your model’s performance.

References:

(1) “An Introductory Guide to Quality Training Data for Machine Learning,” Source: https://www.v7labs.com/blog/quality-training-data-for-machine-learning-guide

(2) “Seven types of data bias in machine learning,” Source: https://www.telusinternational.com/articles/7-types-of-data-bias-in-machine-learning

(3) “How to Use Machine Learning to Separate the Signal from the Noise,” Source: https://www.skan.ai/process-mining-insights/how-to-use-machine-learning-to-separate-the-signal-from-the-noise#:~:text=The%20errors%20are%20known%20as,of%20noise%20can%20impact%20datasets.

(4) “Preprocessing with sklearn: a complete and comprehensive guide,” Source: https://towardsdatascience.com/preprocessing-with-sklearn-a-complete-and-comprehensive-guide-670cb98fcfb9

(5) “A survey on Image Data Augmentation for Deep Learning,” Source: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0