Cheatography
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This cheatsheet will give you a sneak peek and basic understanding of why and how to encode categorical variables by using scikit learn library. It will show you 3 types of encoding One-Hot Encoding, Ordinal Encoding and Label Encoding.
This is a draft cheat sheet. It is a work in progress and is not finished yet.
Why do we Encode?
- Most of the models only accept numeric values.
- We cannot afford to loose important features because of their data types.
- It is required to ensure correct and good performance of the model. |
Types of Encoding
- Ordinal Encoding
- One Hot Encoding
- Label Encoding |
Ordinal Encoding
- Used for encoding Ordinal Variables.
- Numbers are assigned to each category based on their order hierarchy of the variable.
- Assigned numbers can be any numbers as long as original order is unchanged.
Code:
!pip install category_encoders
import category_encoders as ce
encoder = ce.OrdinalEncoder(mapping=[{'col': 'feedback', 'mapping': {'bad': 1, 'okay': 2, 'good':3}}])
encoder.fit(X)
X = encoder.transform(X)
X['feedback']
Output:
feedback
1
2
3
2
3
.
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Documentation: https://contrib.scikit-learn.org/category_encoders/ordinal.html
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One-Hot Encoding
- Used when number of categories in the variable are low, max 3 or 4. Anymore will seriously increase the size of your dataset and decrease performance of your model.
- Assigns 0 and 1 to the categories based on their presence in the columns.
- Creates extra columns based on the number of categorical elements in the main column.
i.e if there are 3 categories in the column Shipping - Standard, One Day, Two Day, 3 extra columns are created in place of the original column, 1 for each category and 1 will be assigned for each unique value.
Usage:
import category_encoders as ce
encoder = ce.OneHotEncoder(cols=['Column Name'])
encoder.fit(df)
df = encoder.transform(df)
df['Shipping'] |
Documentation: https://contrib.scikit-learn.org/category_encoders/onehot.html
Label Encoding
- Converts each category in a column to a number directly.
- Can also be used for non-numerical values as long as they are relevant and usable to the target variable.
- Different Methods can be applied according to your requirements.
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['Column Name_Cat'] = le.fit_transform(df['Column Name'])
df |
Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
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