Introduction
In machine learning, it’s crucial to split your dataset into three distinct subsets: training, validation, and test. This practice ensures robust model evaluation and prevents overfitting.
Why Split Data?
- Training Set: Used to train the machine learning model.
- Validation Set: Used to tune hyperparameters and select the best model configuration.
- Test Set: Used to evaluate the performance of the final chosen model on unseen data.
Data Splitting Strategies
1. Percentage-Based Splitting
The most common approach is to split the data based on fixed percentages.
| Set | Percentage |
|---|---|
| Train | 70-80% |
| Validation | 10-15% |
| Test | 10-15% |
2. K-Fold Cross-Validation
For smaller datasets, k-fold cross-validation is beneficial. Here, the data is divided into k folds. The model is trained on k-1 folds and validated on the remaining fold. This process is repeated k times, with each fold serving as the validation set once.
3. Stratified Splitting
When dealing with imbalanced datasets, stratified splitting is essential. It ensures that the proportion of classes in each split is representative of the original dataset.
Code Example (Python with scikit-learn)
from sklearn.model_selection import train_test_split
# Assuming 'X' is your feature data and 'y' is your target data
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2)
# Split the remaining data into validation and test sets
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5)
# Now you have X_train, y_train, X_val, y_val, X_test, y_test
Conclusion
Proper data splitting is essential for building reliable and generalizable machine learning models. By using appropriate strategies, you can ensure your model performs well on unseen data and avoid overfitting.