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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: 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.