Data Archive

Encoding Categorical Variables

Overview

Categorical variables need to be converted into numerical representations to be used in models, particularly in Regression analysis. This process is essential for transforming categorical results into a format that algorithms can interpret.

Label Encoding

This method assigns a unique integer to each category in the variable.

from sklearn.preprocessing import LabelEncoder
 
label_encoder = LabelEncoder()
var1_cat = df['var1']  # Replace df with your DataFrame
var1_encoded = label_encoder.fit_transform(var1_cat)

For example, if df[col] contains the categories ['apple', 'banana', 'orange'], the LabelEncoder would transform them into [0, 1, 2].

However, keep in mind that this encoding can imply an order or hierarchy in the data, which might not be intended. In some cases, you might want to use OneHotEncoder instead, which creates a binary vector for each category.{}

Given a term in the df you can transform it without needing to look up its value.

company="google"
company_n = LabelEncoder().transform([company])

One-Hot Encoding

This technique creates a binary column for each category, allowing the model to treat each category as a separate feature.

from sklearn.preprocessing import OneHotEncoder
 
binary_encoder = OneHotEncoder(categories='auto')
var1_1hot = binary_encoder.fit_transform(var1_encoded.reshape(-1, 1))
var1_1hot_mat = var1_1hot.toarray()
var1_DF = pd.DataFrame(var1_1hot_mat, columns=['cat1', 'cat2', 'cat3'])  # Adjust column names as needed
var1_DF.head()

Understanding OneHotEncoder:

The OneHotEncoder from sklearn.preprocessing is used to convert categorical integer values into a format that can be provided to machine learning algorithms to do a better job in prediction. It creates a binary column for each category and returns a sparse matrix or dense array.

Converting All Categorical Variables to Dummies

To convert all categorical variables in a DataFrame to dummy variables, you can use the following loop:

for col in df.columns:
    if df[col].dtype == 'object':
        dummies = pd.get_dummies(df[col], drop_first=False)
        dummies = dummies.add_prefix(f'{col}_')
        df.drop(col, axis=1, inplace=True)
        df = df.join(dummies)

Dummy Variable Trap: When using one-hot encoding, it’s important to avoid the dummy variable trap, which occurs when one category can be perfectly predicted from the others. To prevent this, you can drop one of the dummy variables, as one column is sufficient to represent a binary choice (0 or 1).

Alternative Encoding Method

Another way to encode categorical variables is by mapping them directly to integers:

dataset['var1'] = dataset['var1'].map({'A': 0, 'B': 1, 'C': 2}).astype(int)
  • Regression: Understanding how regression models utilize encoded variables.
  • Feature Engineering: Techniques to enhance model performance through better feature representation.

Overview

  • Categorical variables need to be converted into numerical representations for use in models. This is essential for transforming categorical data into a format that algorithms can interpret.

Methods

  • Label Encoding: Assigns a unique integer to each category.
  • One-Hot Encoding: Creates a binary column for each category, allowing the model to treat each category as a separate feature.

Example Code

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
 
# Label Encoding
label_encoder = LabelEncoder()
var1_encoded = label_encoder.fit_transform(df['var1'])
 
# One-Hot Encoding
binary_encoder = OneHotEncoder(categories='auto')
var1_1hot = binary_encoder.fit_transform(var1_encoded.reshape(-1, 1))
var1_1hot_mat = var1_1hot.toarray()
var1_DF = pd.DataFrame(var1_1hot_mat, columns=['cat1', 'cat2', 'cat3'])

Graph View