A training split is a way of dividing a dataset into different subsets for the purpose of training and testing machine learning models. The training split is used to train the model, while the remaining data is used to test its performance.

A commonly used training split is 70/30 or 80/20, where 70% or 80% of the data is used for training and the remaining 30% or 20% is used for testing. However, the ideal split depends on the size and complexity of the dataset, as well as the specific requirements of the machine learning problem.

For example, a training split for a Sentiment Analysis Binary Classification (SBD) problem could be:

- 70% of the data for training
- 30% of the data for testing
This split can be created by randomly selecting a portion of the data for training and using the remaining data for testing. The aim is to create a representative split that provides a good balance between training data for model fitting and test data for evaluation.
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