Subsampling the “right” amout of data to train an ML model
Every Machine Learning Problem is different, so there is no standard answer to your question. For the problem you're working on maybe a 70-30 train-test split would result in an optimal model which performs equally well on the test dataset, whereas for another problem may be that ratio just won't do any justice to the model. It's all about experimentation.
Basically, While training your model, you are basically trying to teach the model all relations, dependencies among attributes which can help the model create a clearer decision boundary between data points associated with your response variable.
Less training may not achieve the task as the model may not learn the underlying structure of the data and hence how the attributes are linked to the response variable, too much training data can have adverse affects too as then you may overfit. I would recommend that start from a 50-50 split record the model performance based on the metric you chose and then repeat the exercise for 60-40, 70-30 etc.