What are the considerations during the train test validation split?
I am currently working on a machine learning project and my job is to project for document classification by using keywords. By considering the nature of legal documents what should be the approach for me to categorize legal documents into different types based on specific keywords present in the text?
There are several considerations when a train test validation split happens in the context of legal document classification. Firstly, you have to remember that given the potential variation in the length of the document and complexities, it is very crucial to allocate a substantial or enough portion to the training set. This allows the model to create a relationship between the two important corner keywords and document types. If you want to fine-tune the model, around 15% should be a validation set. It will enable adjustments for the specific legal language intricacies. The remaining 15% is reserved for the test set will ensure an unbiased evaluation of the unseen documents. It will ensure the ability of the model to approach diverse legal language patterns. Follow the data science certification online course for more knowledge on validation split.