The accepted standard for data separation follows an 80/20 split but the ratios need adjustment considering both the database size and model intricacy and functional application needs. The method of effectively dividing your dataset has likely been a concern if you have ever completed a machine learning task. The absence of formal guidelines permits best practices to assist users in developing their final decision.
This blog focuses on various data split methods alongside appropriate train-validation-test split usage examples and proves effective approaches for handling unbalanced datasets. The following guide includes a detailed look at cross-validation approaches while offering manual code implementations with displayed results. So, let’s get started!
Table of Contents
Why Do We Need to Split a Dataset?
Machine learning requires training models on specific data fractions to check their performance using separate data parts. This helps to ensure that:
- During training the model identifies patterns contained within the provided data.
- Unseen information can be processed successfully by the model.
- The modeling process prevents the memorization of training data instead of true learning.
Common Dataset Splitting Strategies:
| Split Type | Purpose | Example |
| Training Set | Used to train the model. | 70-80% |
| Validation Set | Used for model tuning and hyperparameter optimization. | 10-20% |
| Test Set | Used for final evaluation. | 10-20% |
Methods to Split Data in a Dataset
Given below are the few methods that are used to split data in a dataset.
Method 1: Basic Train-Test Split (80-20) using train_test_split()
A common rule of thumb is an 80-20 split, where:
- 80% of the data is used for training
- 20% is reserved for validation/testing.
Example:
Output:
Explanation:
The provided code operates to break down a dataset between training at 80% and validation at 20%. Through train_test_split() functions the script generates print statements of its output.
Method 2: Stratified Sampling for Imbalanced Datasets
The stratify parameter keeps class ratios equal between all splits for uneven datasets.
Example:
Output:
Explanation:
With stratified splitting the training set and validation set preserve identical distributions of classes.
Method 3: K-Fold Cross-Validation for Small Datasets
Each sample within K-Fold Cross-Validation serves two purposes for the training and validation stages.
Example:
Output:
Explanation:
The different subsets of data undergo tests in separate folds as part of a standardized evaluation scheme.
Method 4: Splitting Data for Deep Learning (Training-Validation-Test Split)
For deep learning, it is common to use the split ratio of 80:10:10.
Example:
Output:
Explanation:
The strategy reserves training data for the model’s learning process along with testing data that will be used for the final assessment.
How Dataset Size Affects the Split Ratio?
The choice of the split of the dataset depends on its size.
- Small datasets (<= 10,000 samples) -> 90% train, 10% validation (to maximize training data).
- Medium datasets (10,000 – 100,000 samples) -> 80% train, 20% validation (default rule).
- Large datasets ( > 100,000 samples) -> 70% train, 15% validation, 15% test (more data for evaluation).
Example:
Output:
Explanation:
The above function helps to split datasets of different sizes into training and validation sets with customizable proportion sizes. The function applies its operations to a dataset of medium size before printing the final set of size measurements.
Train-Test-Validation Split: When Should You Use It?
The train-test-validation split is used when:
- Tuning hyperparameters (validation set prevents overfitting).
- Ensuring unbiased model evaluation (test set acts as unseen data).
- Large datasets (can afford separate validation and test sets).
Example:
Output:
Explanation:
The presented Python code executes data partitioning that divides 10,000 samples into training with 80% samples, validation with 10% samples, and the remainder in the test set. The train_test_split technique in scikit-learn helps perform this operation.
Handling Time-Series Data: Why Random Splitting Won’t Work
Many Time-series datasets present information with chronological arrangement. The implementation of random splits on chronological data proves ineffective because it mixes past data with upcoming data thus producing leaks in the data. You can conduct sequential splits since they involve training with past data while validating with data from the future.
Example:
Output:
Explanation:
The above statement generates a dummy time-series dataset using 100 dates along with random values. The data gets split into training and validation segments with 80% and 20% respectively based on chronological order.
Dealing with Highly Imbalanced Datasets
Random dataset splitting does not guarantee the preservation of class distributions so it can lead to poor generalization abilities of models. Stratified sampling enables you to preserve class distribution consistency between train and validation divisions.
Example:
Output:
Explanation:
We established an imbalanced dataset which included class 0 at 90% and class 1 at 10% using the above script. The code conducts stratified train-validation splitting to preserve class distributions and displays the distributions.
Leave-One-Out Cross-Validation (LOO-CV): When to use and Why?
LOO-CV demonstrates value in small data cases due to its capability to divide data samples into numerous training groups. The training process performs only once while using one data point followed by testing on the remaining data point during total point repetitions.
Example:
Output:
Explanation:
The code executes Leave-One-Out Cross-Validation (LOO-CV) on the Iris dataset through its implementation. The method applies Logistic Regression as its model to fit all samples except one for each iteration. The code determines the average accuracy in addition to performing the calculations.
How to Detect and Prevent Data Leakage?
When information from the validation/test set influences model training, it leads to data leakage. A common mistake is normalizing the entire dataset before splitting instead of applying transformations only to training data.
Example: Incorrect Code (Data Leakage)
Explanation:
The above code normalizes the entire dataset incorrectly before splitting, which can lead to data leakage. Instead of fitting the StandardScaler on the training set only and then transforming both training and testing sets separately, the output that you get will be a standardized dataset, where each feature has a mean of 0 and a standard deviation of 1.
Example: Correct Code (No Leakage)
Explanation:
The above code is used to correctly split the dataset into training and test sets. It then applies StandardScaler by fitting it only on the training data to make sure that it prevents data leakage. It uses the same transformation on the test set for consistent scaling.
Conclusion
A machine-learning model needs proper dataset splitting to develop robustness. A starting point for adopting the 80-20 splitting rule exists but the optimal solution depends on several vital factors including dataset size and model complexity and problem category. Small datasets can be properly evaluated through k-fold and leave-one-out strategies and imbalanced data needs stratified sampling. The approach of chronological data splitting becomes necessary to avoid data leakage in time-series data. Correct transformation application enables bias reduction thus leading to improved generalization together with enhanced model performance. Trying various strategies will assist you in discovering a suitable solution for the particular dataset.
FAQs:
1. What is the general rule of thumb for splitting a dataset into training and validation sets?
The general rule of thumb for splitting data into training and validation sets is the 80-20 split, where 80% of the data is used for training and 20% is used for validation. This can vary based on the size of the dataset, model complexity, and problem type.
2. How should I split my dataset if it is small?
If your dataset is small, you can split you can split it using k-fold cross-validation or leave-one-out cross-validation (LOO-CV). This helps to maximize training data while ensuring reliable validation
3. What is the best way to handle imbalanced datasets when splitting?
To handle imbalanced datasets while splitting the best way is to use stratified sampling which helps in ensuring that both training and validation sets maintain the same class distribution as the original dataset.
4. How should I split time-series data for training and validation?
For splitting time-series data for training and validation you should split it chronologically, which ensures that past data is used for training while future data is reserved for validation. This helps to prevent data leakage.
5. Should I apply preprocessing (e.g., normalization) before or after splitting the data?
You should apply preprocessing (like normalization or scaling) after splitting the data. This should be done only on the training set. You can also use the same transformation on the validation set which helps to prevent data leakage.