Understanding Train, Validation, and Test Sets in Machine Learning
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Chapter 1: Introduction to Machine Learning Sets
Greetings, dear reader! In this article, we will delve into the concepts of training, validation, and test sets within the realm of machine learning. To clarify these distinctions, I'll provide a relatable, everyday example. Are you ready? Let’s dive in!
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Section 1.1: The Need for Data in Machine Learning
Machine learning models acquire knowledge through experience, which means they require data for training. Additionally, they need data for evaluation purposes. Without this, how could we determine if our model has effectively learned from the training data and if it can apply its knowledge to new, unseen data?
When data scientists prepare a dataset for training a machine learning model, they typically divide it into three segments (though in some cases, two may suffice, especially during cross-validation). These segments are:
- Training Set: This portion is used for training the machine learning model. It contains the data from which the model learns and attempts to generalize to new, unseen instances.
- Validation Set: This subset is utilized for evaluating the model during the training phase. It helps in fine-tuning algorithms and adjusting hyper-parameters to determine the most effective combination for the model’s performance.
- Test Set: This is the final assessment set used to gauge the model’s performance after training and selection of the optimal hyper-parameters. It serves to evaluate how well the model performs on previously unseen data.
Disparities in performance between the training and test or validation sets can indicate whether the model is suffering from overfitting or underfitting, although that topic warrants further exploration.
Section 1.2: Real-Life Example of Train, Validation, and Test Sets
To make these concepts relatable, let’s consider a real-life scenario, such as preparing for a significant exam—be it the GMAT, a yearly medical assessment, or university finals. Choose the context that resonates with you.
As you study, you engage with theory, read textbooks, and tackle various problems and exercises. The exercises you work on represent your training set.
To enhance your chances of success, you attend an academy weekly, where an instructor administers practice exams that closely resemble the actual test. These assessments provide feedback on your progress and allow you to adjust your study strategies, serving as your validation set.
Finally, you face the ultimate challenge: the actual exam. After extensive preparation using the training set and refining your approach through the validation set, you are now ready to tackle the test set.
Does that make it clearer?
Conclusion
In this article, we explored the definitions of training, validation, and test sets in machine learning and illustrated these concepts with a straightforward example. I hope you found this discussion helpful, and I wish you all the best in your educational journey.
For more tips and articles like this, follow me on Twitter and explore the linked resources. Have a fantastic day and enjoy your journey in AI!
The first video, "Machine Learning: Validation vs Testing," provides insights into the critical differences between validation and testing in machine learning.
The second video, "Train, Test, & Validation Sets | How to Train Machine Learning Models (Properly!!!)," explains the importance of these sets in training machine learning models effectively.