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When using a train and test set for machine learning, should I apply preprocessing steps such as scaling and imputation to the entire training set before splitting it into train and validation subsets, or should I split the training set first and then perform preprocessing on the training subset before evaluating the model on the validation set?"
Yes, it is possible to preprocess the training set first and then create a validation set from it. This is a common approach in machine learning where you want to perform preprocessing steps such as scaling, normalization, or imputation on your data before splitting it into training and validation sets. The reason for this is to ensure that your validation set is a fair representation of new, unseen data and that it has not been used to inform any decisions during the preprocessing step.
The general steps for this approach are:
By performing preprocessing on the entire dataset before splitting, you can ensure that the preprocessing is consistent across both the training and validation sets. This can help to prevent data leakage and ensure that your model's performance is representative of its true generalization ability.
However, it's important to note that the specific approach to preprocessing may depend on the nature of your data and the specific machine-learning algorithm being used. In some cases, it may be necessary to perform preprocessing on the training set only or to use more complex preprocessing techniques such as cross-validation.
Running random forest algorithm with one variable