How to Fix Encoding Issues with Target Column in Multiclass Classification Using Pandas

Learn how to solve encoding issues in your target column for multiclass classification problems using Pandas with clear, step-by-step guidance.
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This video is based on the question https://stackoverflow.com/q/66624129/ asked by the user 'AMIT BISHT' ( https://stackoverflow.com/u/6816356/ ) and on the answer https://stackoverflow.com/a/66625039/ provided by the user 'Anurag Dabas' ( https://stackoverflow.com/u/14289892/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Fix Encoding Issues with Target Column in Multiclass Classification Using Pandas

When working on a multiclass classification problem, it's common to encounter issues with encoding the target column. A user recently faced a perplexing situation where encoding efforts yielded only one category. In this post, we will explore the problem at hand and outline a structured solution that ensures your target column is encoded correctly.

Understanding the Problem

In the user’s case, the target column consisted of four distinct classes: Low, Medium, High, and Very High. However, after attempting to encode these classes into numerical values, the resulting value counts indicated only one category—0.

Here's a brief summary of the original data structure:

High: 18,767 instances

Very High: 15,856 instances

Medium: 9,212 instances

Low: 5,067 instances

Despite having a diverse dataset, the encoding attempts resulted in:

0: 48,902 instances.

The user was clear that they aimed to achieve an encoding of 0, 1, 2, 3 for these classes but faced difficulties across various encoding methods: replace(), factorize(), and Label Encoder.

Analyzing the Encoding Methods

1. Replace Method

The user initially tried to replace string labels with numeric values using a dictionary mapping. However, this method led to incorrect results.

2. Factorize Method

The use of factorize() is often a good approach but can yield similar issues if the data hasn't been correctly prepped.

3. Label Encoder

Employing LabelEncoder from sklearn typically works well for label encoding, but here it also failed to represent the different classes appropriately.

Finding a Solution

The encoding issues primarily stem from incorrect handling of the data types or the conversion method used. Let's break down the solution step-by-step.

Step 1: Define a Mapping

First, you need to create a mapping dictionary to correlate the class names with their respective numeric values.

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Step 2: Ensure Correct Data Type

Next, ensure that your target column is treated as an object type. This can prevent unintended interactions during encoding.

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Step 3: Create a Custom Encoding Function

Define a function that uses the mapping dictionary to convert class names into numeric values.

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Step 4: Apply the Function

Now, apply this function to your target column using apply(). This method is effective for transforming each entry based on your custom logic.

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Step 5: Verify the Output

Finally, it’s essential to check the output of your transformations by examining the value counts.

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Conclusion

By following these steps, you should be able to encode your target column properly, resulting in a numeric representation of 0, 1, 2, 3 as intended. Encoding is a crucial part of preparing your dataset for multiclass classification, and understanding the intricacies of methods like replace(), factorize(), and custom functions will smooth your data preprocessing journey.

Now, when tuning your model or running analyses, you’ll be confident in the integrity of your target column.

Happy coding!