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Label Encoding.txt
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Label Encoding.txt
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Label Encoding
Label encoding is a method of encoding categorical variables as integers. It is typically used for encoding ordinal variables, where the order of the categories has meaning.
# Import the LabelEncoder class
from sklearn.preprocessing import LabelEncoder
# Create a LabelEncoder object
le = LabelEncoder()
# Fit the encoder to the categorical data
le.fit(df['cat_col'])
# Transform the categorical data to integers
df['cat_col_encoded'] = le.transform(df['cat_col'])
Key Takeaways
Label encoding should only be used for ordinal variables, where the order of the categories has meaning.
Label encoding can create a ranking among the categories, which may not be appropriate if the categories are not truly ordinal.
It is important to avoid creating a ranking if the categories are not ordinal, as this can introduce bias into the model.
Best Practices
Use one-hot encoding for categorical variables with no ordinal relationship.
Consider using ordinal encoding for categorical variables with an ordinal relationship, as this can preserve the ordinal relationship while avoiding the creation of a ranking.
If using label encoding, make sure to clearly communicate the meaning of the encoded values to others who may be working with the data.