Training Data
- Purpose: Used to teach (train) the model.
- Contents: Contains both input features and corresponding output labels (in supervised learning).
- Usage: The model learns patterns, relationships, and parameters from this data.
- Size: Typically the largest portion of the dataset (e.g., 70–80%).
Example:
If you’re training a model to recognize handwritten digits:
- Input: Images of digits
- Label: The digit (0–9)
Test Data
- Purpose: Used to evaluate how well the model performs on unseen data.
- Contents: Same format as training data (features + labels), but not used during training.
- Usage: Helps assess model accuracy, generalization, and potential overfitting.
- Size: Smaller portion of the dataset (e.g., 20–30%).
Key Point: It simulates real-world data the model will encounter in production.
Validation Data
- Purpose: Used to tune the model’s hyperparameters and monitor performance during training.
- Contents: Same format as training/test data — includes input features and labels.
- Usage:
- Helps choose the best version of the model (e.g., best number of layers, learning rate).
- Detects overfitting early by evaluating on data not seen during weight updates.
- Not used to directly train the model (no weight updates from validation data).
Summary Table
| Aspect | Training Data | Validation Data | Test Data |
|---|---|---|---|
| Used for | Training model | Tuning model | Final evaluation |
| Used during | Model training | Model training | After model training |
| Updates model? | Yes | No | No |
| Known to model | Yes | Seen during training | Never seen before |
Tip:
In practice, for small datasets, we often use cross-validation, where the validation set rotates among the data to make the most of limited samples.
Typical Size Ranges for Small Datasets
| Dataset Type | Number of Samples (Roughly) |
|---|---|
| Very Small | < 500 samples |
| Small | 500 – 10,000 samples |
| Medium | 10,000 – 100,000 samples |
| Large | 100,000+ samples |
Why Size Matters
- Small datasets are more prone to:
- Overfitting – model memorizes data instead of learning general patterns.
- High variance in performance depending on the data split.
- Big models (e.g., deep neural networks) usually need large datasets to perform well.
💡 Common Examples
- Medical diagnosis: Often < 5,000 patient records → small dataset.
- NLP for niche domains: < 10,000 labeled texts → small.
- Handwritten digit dataset (MNIST): 60,000 training images → medium-sized.
🔁 Tip for Small Datasets
If your dataset is small:
- Use cross-validation (like 5-fold or 10-fold).
- Consider simpler models (e.g., logistic regression, decision trees).
- Use data augmentation (e.g., rotate/scale images, reword texts).
- Apply transfer learning if using deep learning (e.g., pre-trained models like BERT, ResNet).
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