Train your model on different train test ratio and note down their accuracies? What to do in this question?

To answer this question, you would need to follow these steps:

Choose different train-test ratios (e.g. 80-20, 70-30, 60-40, etc.)

Split your dataset into training and testing sets based on the chosen ratios

Train different machine learning models (e.g. decision trees, random forests, SVM, etc.) on the training set

Evaluate the models on the test set and note down their accuracy scores

Compare the accuracy scores of the models and choose the one with the highest accuracy.

This process allows you to see the effect of the train-test split on the model's performance and helps you determine an optimal split for your specific use case.