Data set splitting is the process of dividing a dataset into two or more separate subsets for training, testing, and validation purposes. The primary purpose of this method is to evaluate the performance of machine learning models using previously unseen data, helping to prevent the model from overfitting or underfitting.
For example, imagine a dataset of customer information for an online e-commerce store. The dataset contains multiple features such as age, gender, buying history, and product preferences. The store wants to build a machine learning model that predicts and recommends the products that customers are most likely to buy.
To accomplish this, the store will divide the dataset into two or more subsets - a training set (used to train the model), a testing set (used to evaluate the trained model), and possibly a validation set (used to fine-tune the model’s hyperparameters). The model then trains on the training set before making predictions on the testing set. The performance of the model is evaluated based on its accuracy in predicting the products that customers are most likely to buy, the recall rate for each class, and other standard metrics.
By splitting the dataset, the e-commerce store can ensure their model generalizes well and performs well on unseen data. It also helps to prevent the model from becoming over-reliant on specific features, which can lead to biased models that don’t represent the entire population.
Data set splitting involves dividing a larger data set into two or more smaller subsets for training and testing machine learning models.
The most common types of data set splitting are train-test split, k-fold cross-validation, and stratified sampling.
Train-test split involves randomly dividing the data into a training set and a testing set. The model is trained on the training set and evaluated on the testing set.
K-fold cross-validation involves dividing the data into k equal subsets, using k-1 for training and the remaining subset for testing. This is repeated k times, with each subset used once for testing.
Stratified sampling ensures that the proportions of each class or category are maintained in each subset, which is important for imbalanced datasets.
The key objectives of data set splitting are to evaluate and compare the performance of different machine learning models, assess their generalization ability, and prevent overfitting.
The choice of the appropriate data set splitting method depends on several factors, such as the size and complexity of the dataset, the number of samples, or the number of classes.