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Split the dataset We can use the train_test_split to first make the split on the original dataset. Then, to get the validation set, we can apply the same function to the train set to get the validation set. In the function below, the test set size is the ratio of the original data we want to use as the test set.
That's why you need to split your dataset into training, test, and in some cases, validation subsets. In this tutorial, you've learned how to: Use train_test_split() to get training and test sets. Control the size of the subsets with the parameters train_size and test_size.
By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model. After a model has been processed by using the training set, you test the model by making predictions against the test set.
Upon some research I found two functions in MATLAB to do the task:
cvpartition function in the Statistics Toolboxcrossvalind function in the Bioinformatics ToolboxNow I've used the cvpartition to create n-fold cross validation subsets before, along with the Dataset/Nominal classes from the Statistics toolbox. So I'm just wondering what are the differences between the two and the pros/cons of each?
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