Blog Post

Data Split: Training, Validation, and Test Sets

Imagine medical students prepared for their final exams by only studying the exact questions that will be on the test. Obviously no one would want to see that kind of doctor!

This metaphor illustrates how using the same dataset for both training and evaluation can lead to a false sense of accuracy, as the model is only learning to perform well on a specific set of data it has already seen, rather than learning to generalize its knowledge to new, unseen data.

For models that perform as designed, in real-world environments, data needs to be effectively split between training, validation, and testing. 

The Training Set: Foundation of Model Learning

The data used for the training set is the engine room of the model; it’s the largest portion of the data, and is used to teach the model to recognize patterns and make predictions.

For example: consider a machine learning model being trained to recognize handwritten digits. The training set would consist of thousands of labeled images of handwritten symbols. The model learns to associate specific pixel patterns with each digit during training.

The Validation Split: The Model’s Tuning Ground

The validation split plays a critical role in the train/test/validation split process. It should be a subset of the data, separate from the training set, used for fine-tuning model parameters and helping prevent overfitting.

The Test Set: The Ultimate Challenge

This independent dataset is not used during the training or validation phases. It serves to provide an unbiased evaluation of the final model’s performance.

Going back to our handwritten digits, the test set would consist of a fresh set of handwritten symbols, ones that have not previously been exposed to the model. This set is used to assess how well the model can identify handwritten digits on data it hasn’t seen before, reflecting its ability to generalize.

Balancing the Splits

Achieving the right balance in the data split is essential. A common split ratio is 70% training, 15% validation, and 15% test. However, this can vary based on the size and nature of the dataset.

For a small dataset, a larger percentage might be allocated to the training set to ensure the model has enough data to learn from, while for larger datasets, more data can be allocated to validation and testing to ensure robustness.

Common Pitfalls in Data Splitting

Several common pitfalls can undermine the effectiveness of the data split process in machine learning.

  1. Data leakage: This occurs when information from the test set inadvertently influences the training process. It can lead to overly optimistic performance estimates.
  2. Overfitting the validation set: Continuously tuning the model based on validation set performance can lead to overfitting on this set, reducing the model’s ability to generalize to new data.
  3. Neglecting data preprocessing consistency: It’s crucial to apply the same data preprocessing steps (like normalization, encoding) uniformly across all splits to avoid skewed results.
  4. Inadequate test set size: Having a test set that’s too small can lead to unreliable performance metrics, as the test set may not capture the model’s ability to generalize effectively.

Best Practices for Data Split in Machine Learning

When implementing your data split in machine learning, these best practices can ensure the effectiveness and reliability of your model.

  1. Representative Sampling: Ensure that each split (training, validation, and test) is representative of the overall dataset. 
  2. Randomization: Randomly splitting the dataset helps in avoiding bias. This ensures that the training, validation, and test sets don’t have underlying patterns that could skew the model’s learning process.
  3. Consistent Data Split Ratio: While the exact ratio can vary depending on the dataset size and nature, maintaining a consistent ratio across different projects or experiments aids in comparability and reproducibility.
  4. Cross-Validation: In cases of limited data, use cross-validation techniques (rotating the validation set through different subsets of the data)
  5. Stratification: When dealing with imbalanced datasets, stratify the splits. 
  6. Temporal Considerations: For time-series data, ensure that the split respects chronological order (i.e. data from the future should not be used to predict past events).

Set Up Models Efficiently and Autonomously

Instead of going through this manual process of training, validating, and testing – and being exposed to the pitfalls that this process entails – there is a much better way.

This way is more accurate, more seamless, more flexible and more scalable. 

It’s Tasq.ai’s automated data pipeline, powered by Tasq.ai’s next-gen Decentralized Human Guidance solution.

With Tasq.ai’s platform, you can set up your model by customizing every aspect, with a simple drag and drop interface; decide what elements to add, set up training, validation and testing, and set your model to continuously optimize. 

Add human validation at key stages, and you really start to feel the magic. This isn’t any human validation: it’s access to the global crowd of Tasqers, with the necessary geographical and skill-based backgrounds to provide meaningful feedback.

To learn more about incorporating Tasq.ai into your ML workflows, get in touch with the Tasq.ai experts today.

The latest blog

Tasq.ai works with leading GenAI companies, enterprises, and government agencies.