How to inverse transform both ordinal and label encoding?
Tags: python, machinelearning, encoding
Published: 2023-08-12 02:02:06

How to Inverse Transform Both Ordinal and Label Encoding?
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When working with categorical data in machine learning, it is common to use encoding techniques to convert the data into numerical form. Two popular encoding methods are ordinal encoding and label encoding. While these techniques are useful for training models, there may be instances where you need to reverse the encoding process and convert the numerical values back into their original categorical form. In this article, we will explore how to inverse transform both ordinal and label encoding.

### 1\. Inverse Transforming Ordinal Encoding

Ordinal encoding is a technique where categorical values are assigned a unique integer value based on their order. For example, if you have three categories: "low", "medium", and "high", they can be encoded as 0, 1, and 2 respectively. To reverse this encoding and convert the numerical values back into their original form, you can create a mapping dictionary that maps the encoded values to their corresponding categories.

 `# Example mapping dictionary for ordinal encoding
                    ordinal_mapping = {0: 'low', 1: 'medium', 2: 'high'}
                    
                    # Inverse transform using the mapping dictionary
                    original_values = [ordinal_mapping[encoded_value] for encoded_value in encoded_data]` 

By using the mapping dictionary, you can easily inverse transform the ordinal encoded data and obtain the original categorical values.

### 2\. Inverse Transforming Label Encoding

Label encoding is another technique where each category is assigned a unique integer value. Unlike ordinal encoding, label encoding does not consider any ordering or hierarchy among the categories. To inverse transform label encoded data, you can use the inverse_transform method provided by various machine learning libraries, such as scikit-learn.

 `from sklearn.preprocessing import LabelEncoder
                    
                    # Create an instance of LabelEncoder
                    label_encoder = LabelEncoder()
                    
                    # Fit the label encoder on the encoded data
                    label_encoder.fit(encoded_data)
                    
                    # Inverse transform the encoded data
                    original_values = label_encoder.inverse_transform(encoded_data)` 

The inverse_transform method of LabelEncoder reverses the encoding process and returns the original categorical values.

### Conclusion

Both ordinal and label encoding are useful techniques for converting categorical data into numerical form. However, it is important to be able to reverse the encoding process when necessary. By following the methods described in this article, you can easily inverse transform both ordinal and label encoded data, and obtain the original categorical values.

### References

*   Scikit-learn documentation: [LabelEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)