Data splitting in modeling

In machine learning, the process of splitting a dataset into different subsets is a fundamental step for training, validating, and evaluating models. The most common splits involve dividing the data into training, validation, and test sets. Here are the key concepts related to data splitting in machine learning:

1. Training Set:
   • Purpose: The primary subset of the data used to train the machine learning model. The model learns patterns and relationships within this set.
   • Percentage of Data: Typically, the training set constitutes a majority of the data, often around 70-80%.
2. Validation Set:
   • Purpose: A separate subset of the data used to fine-tune model hyperparameters and assess its performance during training. It helps prevent overfitting to the training data.
   • Percentage of Data: Usually, a smaller portion of the data, around 10-20%.
3. Test Set:
   • Purpose: A completely independent subset of the data that the model has never seen during training or validation. It is used to evaluate the model’s performance on new, unseen data.
   • Percentage of Data: Similar to the validation set, around 10-20%.
4. Cross-Validation:
   • Purpose: An alternative to a single train-test split, cross-validation involves multiple rounds of training and evaluation. The dataset is divided into K folds, and the model is trained and validated K times, with each fold serving as the test set once.
   • K-Fold Cross-Validation: One common technique where the dataset is divided into K equally-sized folds.
5. Stratified Sampling:
   • Purpose: Ensures that the distribution of classes in each subset (train, validation, test) is representative of the overall dataset. This is particularly important for imbalanced datasets.
   • Stratified K-Fold Cross-Validation: Combines K-fold cross-validation with stratified sampling.
6. Random Sampling:
   • Purpose: Randomly dividing the dataset into different subsets, ensuring that each data point has an equal chance of being in any set.
   • Shuffling: Before splitting, the dataset is often shuffled to remove any inherent order.
7. Time-Based Splitting:
   • Purpose: When dealing with time-series data, the dataset is often split based on chronological order. The training set includes data up to a certain point in time, the validation set covers the next time period, and the test set represents future data.
   • Preventing Data Leakage: Ensures that the model is evaluated on its ability to predict future observations.
8. Holdout Set:
   • Purpose: Similar to the test set, the holdout set is a portion of the data reserved for final model evaluation. It is not used during training or hyperparameter tuning.
   • Separate from Test Set: In some cases, the holdout set is distinct from the test set, allowing for additional evaluation on completely unseen data.

Data splitting is crucial for assessing a model’s performance, preventing overfitting, and ensuring that the model can generalize well to new instances. The choice of splitting strategy depends on the nature of the data, the machine learning task, and the available resources.