Should I impute test data?
You should never infer information from test dataset as that’s an information leak. Calculating mean of test dataset would give your algoritm info about mean of it (obviously) and would probably falsely improve it’s score on said.
How do you handle missing value in test data?
This article covers 7 ways to handle missing values in the dataset:
- Deleting Rows with missing values.
- Impute missing values for continuous variable.
- Impute missing values for categorical variable.
- Other Imputation Methods.
- Using Algorithms that support missing values.
- Prediction of missing values.
What is iterative Imputer?
Iterative imputation refers to a process where each feature is modeled as a function of the other features, e.g. a regression problem where missing values are predicted.
How do you deal with missing data in a test set?
How to deal with missing values in ‘Test’ data-set?
- Replacing them with mean/mode.
- Replacing them with a constant say -1.
- Using classifier models to predict them. No idea about SAS but R provides various packages for missing value imputation like kNN, Amelia.
What happens when you impute both training and testing?
If you fit imputation on both training and testing, then any new testing dataset requires you to re-impute all data again, and this allows leaking information/feature into the model because the information from testing dataset is included in training the model, and consequently, your model won’t be able to predict a new data.
When to impute missing values in training set?
Keeping the past/future analogy in mind, this means anything you do to pre-process or process your data, such as imputing missing values, you should do on the training set alone. You can then remember what you did to your training set if your test set also needs pre-processing or imputing, so that you do it the same way on both sets.
Is it OK to impute missing values with the mean?
Yes. It is fine to perform mean imputation, however, make sure to calculate the mean (or any other metrics) only on the train data to avoid data leakage to your test set. Is it ok to impute mean based missing values with the mean whenever implementing the model?
When to impute before or after cross validation?
In this case, if you impute first with train+valid data set and split next, then you have used validation data set before you built your model, which is how a data leakage problem comes into picture. But you might ask, if I impute after splitting, it may be too tedious when I need to do cross validation.