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Train your model on different train test ratios and note down their accuracies.
Check for 90-10, 80-20, and 70-30 ratios it would help … finding the three best pair and their accuracies
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.
Running random forest algorithm with one variable