Training, validation, and test datasets. What is the difference?

Comparing training, validation, and test sets

Let’s overview the differences between training, validation, and test sets. All of these datasets have their own distinctive roles in the life cycle of a machine learning model.

1. The training set is primarily employed in the initial training phase – it’s used to teach the model patterns and behaviors embedded in the dataset.
2. In contrast, the validation set helps fine-tune the model after the initial training phase by adjusting hyperparameters, detecting and correcting overfitting issues during training.
3. Finally, the test set is reserved for the final stage of the ML process: it helps evaluate how well the model performs in real-world scenarios by providing an unbiased assessment of its generalization capabilities in terms of new data.

While these sets have their differences, they also share some common points, especially regarding the indispensable role they all play throughout the machine learning process. All three sets are usually extracted from a single initial dataset via data splitting.

Training, validation, and test sets are essential for developing, refining, and assessing the model to ensure effective learning, correct generalization, and consistent performance across unseen situations. Maintaining the integrity and coherence of these datasets is critical as they collectively contribute to creating a well-rounded machine learning model.

Training, validation and test datasets – core differences

Let’s explore these types of datasets in more detail.

Training data

Definition and role of training data in ML

The training data is a source of information for ML model’s learning. This set covers a range of input features, each matched with corresponding target labels or outcomes, allowing supervised learning algorithms to deduce connections and patterns from the data.

The purpose of training data is twofold: it offers examples for the model to learn from and fine-tunes the model’s parameters to reduce prediction errors. Through iterative processes like backpropagation and gradient descent, the model continuously adjusts its parameters to enhance predictive accuracy.

Characteristics of training datasets

Training sets are usually quite large: they contain thousands to millions of observations to ensure a diverse representation of the underlying data distribution. This helps the model learn from a wide range of examples and then apply its knowledge to unfamiliar data effectively. As we’ve mentioned, training sets come with labels for each known target value or outcome. Data annotation is invaluable for teaching the model how to link input features with target outcomes.

Moreover, it’s crucial for training sets to mirror real-world scenarios that the model is intended to encounter in the future. Those representations should be applicable and generalizable across different contexts. This can be achieved by selecting and curating training data that captures the range of variability and complexity present in the target domain.

These datasets may include noise, outliers, or missing values, which require preprocessing and cleaning techniques to improve data quality and reliability. Overcoming these obstacles helps practitioners ensure that their training data adequately supports learning processes and aids in building precise machine learning models.

How is training data prepared and preprocessed?

Preparing and preprocessing training data are two crucial steps in the ML process that directly influence the quality and performance of models. Raw data must undergo several processes before being fed into ML algorithms: data cleaning to remove outliers and errors, feature engineering to extract relevant information, and feature scaling to normalize the data distribution.

Moreover, preprocessing may involve managing missing values, encoding categorical variables, and performing dimensionality reduction to boost efficiency and prevent overfitting.

Data augmentation methods can also be used to expand training datasets, especially when labeled data is limited or imbalanced. By adding synthetically generated examples or transformations to the training data, practitioners can improve model robustness and prediction accuracy.

Validation data 

Definition and role in ML

Validation data helps refine models and evaluate their effectiveness. While training data is employed to train the model’s parameters, validation data is used to evaluate the model’s generalization capability and identify potential issues – e.g., overfitting or underfitting.

By setting aside a portion of the data for validation during model training, professionals can make informed choices about selecting models, tuning hyperparameters, and optimizing the overall performance.

Characteristics of validation datasets

Validation sets share some similarities with training sets but still have their distinct characteristics. Similar to training data, validation data includes input features and corresponding target labels, allowing for supervised learning tasks.

However, validation sets are smaller in size than training sets because they are used for evaluation rather than model training. Validation datasets should be representative of the broader data distribution to ensure that the model’s performance reflects its ability to generalize across real-world situations.

How is validation data created?

Creating validation data involves splitting the initial dataset into training and validation subsets. One common method is the holdout approach, where a portion of the data is set aside for validation while the rest is used for training. Another technique is k-fold cross-validation, where the dataset is divided into k equal-sized folds, with each serving as a validation set in turns. These methods help practitioners assess model performance effectively while making use of data. We delve deeper into data splitting in the next chapter. 

Moreover, recent studies have shown the importance of external validation datasets in predictive modeling. For example, in the development of a prediction model for drug response in acute myeloid leukemia, ensemble models validated with external datasets (Clinseq and LeeAML) demonstrated improved prediction accuracy.

These models achieved a higher correlation between predicted and observed drug responses, with significant portions of drugs showing better performance in the ensemble models compared to base models.

Test data

Definition and role in ML

Testing data plays a role in the final assessment phase in the ML process: it provides an unbiased assessment of a trained model’s performance on unseen data. Unlike training and validation datasets, the test set is kept separate until the evaluation stage to maintain objectivity and guarantee the credibility of performance measurements. The main purpose of test data is to determine how well the model can adapt to new instances and evaluate its predictive precision in real environments.

Characteristics of test datasets

Unseen data is included in test sets to evaluate how well the final model will work in real life after it’s put into action. These data sets reflect the overall population and preserve the data distribution to ensure that the performance metrics are reliable and indicative of how the model will perform in actual use.

How to generate test data?

Test datasets are also created using data splitting methods. Test sets remain separate from the training and validation sets during model development to later accurately evaluate the model’s performance on unfamiliar data.

Data scientists might use external datasets or real-world data collected from production environments as test data to evaluate the model’s performance in practical applications. This approach helps confirm that the model can adapt and handle real-world situations beyond what was seen in training and validation phases.

Data Splitting

What is data splitting?

Splitting data is an important practice in machine learning. It includes dividing a dataset into parts for training, validation, and testing. This partitioning enables practitioners to develop and evaluate machine learning models effectively, ensuring reliable performance and generalization to unseen data. By dividing the dataset, experts can evaluate model performance, adjust settings, and confirm results before using the model in real-life tasks.

Methods for data splitting in ML

Random sampling

Random sampling is a process of picking data points from a dataset unsystematically to create training, validation, and test sets. This method is commonly used with diverse datasets. Random sampling ensures that each subset represents the overall data distribution, reducing bias and supporting model training and evaluation.

Stratified dataset splitting

This method maintains the class distribution in each subset: the dataset is divided, but the relative proportions of each class is preserved. This results in training, validation, and test sets, containing a representative subset from each class – this upholds the original class distribution.

Stratified splitting approach is valuable for imbalanced datasets, where some classes may be less common. This is a great method for preventing bias in datasets. 

Cross-validation splitting

This involves dividing the dataset into training and validation sets for model training and evaluation. This technique helps stabilize model performance by averaging results over runs. Popular cross-validation methods include K-fold and stratified k-fold cross-validation.

Holdout method 

With this approach, the dataset is divided into two parts: one for training the model and another for testing its performance. Typically, a larger portion is set aside for training purposes while a smaller portion is kept to evaluate the model. Holdout data splitting method helps to prevent overfitting and ensures that the model is tested on unseen data. 

Time-based splitting

This approach is based on ​​dividing temporal data into training, validation, and test sets based on chronological order. Time-based splitting is used in predictive modeling tasks (e.g., time series forecasting and financial analysis), where historical data is used for training, recent data – for validation, and future data – for testing. This method ensures that models are evaluated using practical information to make accurate predictions.

Studies have demonstrated that the choice of data splitting technique can greatly impact how well a model performs and generalizes. A recent research emphasizes the importance of selecting the right data splitting method to estimate the generalization performance of models. It shows that while all data splitting methods can bring comparable results for large datasets, different methods used for small datasets can dissimilarly impact model performance.

The research finds that no single method consistently outperforms others across all scenarios – the optimal choice of data splitting technique and parameters depends on the data itself. This underlines the importance of balanced distribution of data across training, validation, and test sets to, particularly to avoid overfitting in small datasets and to achieve robust model generalization.

Common mistakes in data splitting

Since data splitting is a fundamental step in ML model development, there are common mistakes that practitioners should be aware of to ensure the reliability and accuracy of their models. Here are some of the most prevalent errors in data splitting:

Data leakage

Data leakage happens when information from the validation or test set accidentally mixes into the training set, resulting in overly optimistic performance estimates. This can occur if preprocessing steps like feature scaling or imputation are applied to the dataset before splitting, rather than separately to each subset.

Imbalanced splitting

This refers to dividing data unevenly across subsets, causing biased model evaluation. This can happen when the dataset contains unequal proportions of classes or categories, and the splitting method doesn’t consider this imbalance. Stratified splitting methods can help address this issue by maintaining class distributions across subsets.

Incorrect evaluation metrics

Using inappropriate evaluation metrics can lead to misleading conclusions about model performance. For instance, accuracy may not be suitable for imbalanced datasets as it can be heavily influenced by the majority class. Data scientists should carefully choose evaluation metrics based on their data’s characteristics and their machine learning task objectives.

Overfitting to validation data

Overfitting happens when the model’s parameters or hyperparameters are repeatedly optimized based on the validation set’s feedback. This can lead the model to learn the specific patterns, noise, or anomalies in the validation data rather than the underlying generalizable features of the dataset. Even though the model might perform great on the validation set, its efficiency may drop when faced with new data (the test set), indicating that it hasn’t really grasped how the predictive relationships. 

Conclusion

In summary the training, validation, and test sets play crucial roles in creating, assessing, and implementing machine learning models. It’s important to understand the distinctions between these subsets and choose the most fitting data partitioning methods to ensure the accuracy and dependability of ML systems.

As machine learning continues to advance, advancements in data splitting techniques will be essential for driving progress and enhancing the development of powerful ML systems.