Troubleshooting tidymodels: Resolving the "Missing Column" Error in Predictions

In this guide, we'll address a common issue in `tidymodels` workflows related to missing columns during prediction. We provide a step-by-step solution to help you resolve the "the following required column is missing from `new_data`" error.
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This video is based on the question https://stackoverflow.com/q/74215751/ asked by the user 'Matt Pickard' ( https://stackoverflow.com/u/12161257/ ) and on the answer https://stackoverflow.com/a/74216096/ provided by the user 'EmilHvitfeldt' ( https://stackoverflow.com/u/4912080/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Troubleshooting tidymodels: Resolving the "Missing Column" Error in Predictions

If you're working with the R package tidymodels and you encounter the frustrating error stating, "the following required column is missing from new_data", you’re not alone! Many users face this issue while trying to make predictions on their test datasets. In this post, we'll identify the root cause of the problem and provide a clear, structured solution.

The In-Depth Problem

When fitting a model using tidymodels, you often use a recipe to preprocess your data, which includes steps like normalization. If your recipe mistakenly includes the outcome variable as a predictor, you'll get an error once you try to predict your test set. The specific error message refers to a column (in this case, price) that is not available during the prediction stage.

Example Scenario

Consider the following R code example, where we are building and fitting a lasso regression model. After fitting the model, when running the prediction on a new dataset, we receive the following error:

[[See Video to Reveal this Text or Code Snippet]]

This clearly indicates that the predictor setup is incorrect due to how we selected the columns in our recipe.

The Solution: Correcting the Recipe

The issue stems from the use of all_numeric() in the step_normalize() function within the recipe. This method selects all numeric columns, including the outcome variable, which leads to a problem during prediction because the outcome variable (price) is not included in the new data that we are predicting against.

Steps to Fix the Error

To resolve this, you should replace all_numeric() with all_numeric_predictors(). Here’s the updated workflow:

[[See Video to Reveal this Text or Code Snippet]]

Important Considerations

all_numeric_predictors(): This function will correctly select only the predictor variables, excluding the target variable (price).

Data Preparation: Ensure your training and test sets are defined properly, and always check your recipe to ensure it only includes the predictors necessary for your model.

Conclusion

By simply adjusting how you reference numeric predictors in your recipe, you can seamlessly resolve the missing column error in tidymodels. This allows for a smoother transition from model training to prediction, helping you get the results you need without unnecessary roadblocks. If you keep running into issues, make sure to double-check your column names and their availability in both training and testing datasets. Happy modeling!

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