What percentage of a machine learning (ML) dataset should be allocated to training?

Allocating 70—80 percent of a machine learning dataset to training is a common practice because it strikes a balance between ensuring that the model has enough data to learn effectively while still reserving a sufficient portion of the data for validation and testing.

When training a model, it is important for the training set to be large enough so that the model can learn the underlying patterns in the data with a good amount of variability. By setting aside 20—30 percent for validation and testing, you enable the model to be evaluated on unseen data. This helps in assessing how well the model generalizes beyond the training dataset.

A training allocation of less than 70 percent may not offer enough data for the model to learn effectively, while going beyond 80 percent could reduce the ability to accurately validate the model’s performance. Therefore, allocating 70—80 percent is a well-established guideline in machine learning tasks.