Why does plotting errors vs actual via PredictionErrorDisplay result in a value error?

Have you ever tried to visualize the errors of your machine learning model using the handy `PredictionErrorDisplay` tool, only to be met with a frustrating `ValueError`? You’re not alone! In this article, we’ll dive into the reasons behind this pesky error and provide you with a step-by-step guide to fix it.

What is PredictionErrorDisplay?

`PredictionErrorDisplay` is a fantastic tool from the `yale` library that helps you visualize the errors of your machine learning model. It takes in the actual values and predicted values of your model and plots them against each other, giving you a clear picture of how well your model is performing. Sounds simple, right?

But what’s going on with the ValueError?

So, why does plotting errors vs actual via `PredictionErrorDisplay` result in a `ValueError`? There are a few reasons for this:

• Incompatible data types: One of the most common reasons for the `ValueError` is that the data types of your actual and predicted values are incompatible.
• Missing or null values: If there are missing or null values in your data, `PredictionErrorDisplay` will throw a `ValueError`.
• Incorrect data structure: If your data is not structured correctly, `PredictionErrorDisplay` won’t be able to plot the errors correctly.

Fixing the ValueError

Okay, so now that we’ve identified the potential causes of the `ValueError`, let’s take a step-by-step approach to fixing it:

Step 1: Check data types

Make sure that your actual and predicted values are of the same data type. You can do this by using the `type()` function in Python:

print(type(actual_values))
print(type(predicted_values))

If they’re not the same, you’ll need to convert one of them to match the other. For example, if your actual values are in a NumPy array and your predicted values are in a Pandas series, you can convert the series to a NumPy array using the `to_numpy()` method:

predicted_values = predicted_values.to_numpy()

Step 2: Handle missing or null values

Next, let’s handle those pesky missing or null values. You can do this using the `dropna()` method from Pandas:

actual_values = actual_values.dropna()
predicted_values = predicted_values.dropna()

This will remove any rows with missing or null values from your data.

Step 3: Check data structure

Now, let’s make sure that your data is structured correctly. `PredictionErrorDisplay` expects your data to be in a specific format, with the actual values in one column and the predicted values in another. You can use the `DataFrame` constructor from Pandas to create a new dataframe with the correct structure:

import pandas as pd

data = {'actual': actual_values, 'predicted': predicted_values}
df = pd.DataFrame(data)

Putting it all together

Now that we’ve fixed the potential issues, let’s put it all together and plot those errors!

from yale.plot import PredictionErrorDisplay

 PredictionErrorDisplay.from_estimator( estimator, 
                                       X=df, 
                                       y=df['actual'], 
                                       ax=ax)

This code creates a `PredictionErrorDisplay` object from an estimator (your machine learning model), the feature data `X`, the actual values `y`, and an axis object `ax`. The `from_estimator` method takes care of the plotting for you.

Conclusion

And there you have it! By following these steps, you should be able to fix the `ValueError` and visualize the errors of your machine learning model using `PredictionErrorDisplay`. Remember to:

• Check that your data types are compatible
• Handle missing or null values
• Check that your data is structured correctly

By following these steps, you’ll be well on your way to creating informative and accurate error plots.

Common causes of ValueError	Solutions
Incompatible data types	Use the `type()` function to check data types and convert if necessary
Missing or null values	Use the `dropna()` method to remove missing or null values
Incorrect data structure	Use the `DataFrame` constructor to create a new dataframe with the correct structure

We hope this article has been helpful in resolving the `ValueError` issue when using `PredictionErrorDisplay`. Happy plotting!

Do you have any other questions about machine learning or data visualization? Let us know in the comments below!

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