Understanding Binarization in Data Preprocessing | by Noor Fatima | Jun, 2024

Binarization is a data preprocessing technique used to transform numerical variables into binary values (0s and 1s) based on a threshold. This method can be particularly useful for converting continuous variables into a form that machine learning algorithms can more easily process.

In this article, we will explore binarization using a practical example involving the Titanic dataset. We will demonstrate how binarization can be applied to a feature, and compare the performance of a machine learning model before and after binarization.

Dataset Overview

For this example, we’ll use a subset of the Titanic dataset, focusing on the following features:

• Age: The age of the passenger
• Fare: The fare paid by the passenger
• SibSp: Number of siblings/spouses aboard
• Parch: Number of parents/children aboard
• Survived: Survival status (target variable)

Step-by-Step Implementation

Let’s walk through the implementation of binarization using Python’s scikit-learn library.

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.compose import ColumnTransformer
# Load and prepare the dataset
df = pd.read_csv('train.csv')[['Age', 'Fare', 'SibSp', 'Parch', 'Survived']]
df.dropna(inplace=True)
# Create a new feature 'family' by combining 'SibSp' and 'Parch'
df['family'] = df['SibSp'] + df['Parch']
df.drop(columns=['SibSp', 'Parch'], inplace=True)
# Split the data into features (X) and target (y)
X = df.drop(columns=['Survived'])
y = df['Survived']


# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Model Training Without Binarization

First, let’s train a Decision Tree classifier without applying binarization.

# Train a Decision Tree classifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
# Predict and evaluate the model
y_pred = clf.predict(X_test)
print("Accuracy without binarization:", accuracy_score(y_test, y_pred))
print("Cross-validation score without binarization:", np.mean(cross_val_score(DecisionTreeClassifier(), X, y, cv=10, scoring='accuracy')))

Applying Binarization

Next, we apply binarization to the family feature using ColumnTransformer and Binarizer.

from sklearn.preprocessing import Binarizer
# Apply binarization to the 'family' feature
trf = ColumnTransformer([
('bin', Binarizer(copy=False), ['family'])
], remainder='passthrough')
X_train_trf = trf.fit_transform(X_train)
X_test_trf = trf.transform(X_test)
# Display the transformed training data
pd.DataFrame(X_train_trf, columns=['family', 'Age', 'Fare']).head()

Model Training After Binarization

Now, we train the Decision Tree classifier again using the binarized data.

# Train a Decision Tree classifier on the binarized data
clf = DecisionTreeClassifier()
clf.fit(X_train_trf, y_train)
# Predict and evaluate the model
y_pred2 = clf.predict(X_test_trf)
print("Accuracy with binarization:", accuracy_score(y_test, y_pred2))
# Cross-validation with binarized data
X_trf = trf.fit_transform(X)
print("Cross-validation score with binarization:", np.mean(cross_val_score(DecisionTreeClassifier(), X_trf, y, cv=10, scoring='accuracy')))

Results and Discussion

By comparing the accuracy scores and cross-validation scores before and after binarization, we can observe the impact of this preprocessing step on the model’s performance.

• Accuracy without binarization: 0.6294
• Cross-validation score without binarization: 0.6429
• Accuracy with binarization: 0.6364
• Cross-validation score with binarization: 0.6304

From the results, we see a slight improvement in accuracy after applying binarization. This suggests that binarization can help in certain cases by simplifying the feature space, making it easier for the model to make decisions.

Binarization is a simple yet effective preprocessing technique that can transform continuous variables into binary ones, potentially improving the performance of certain machine learning algorithms. By applying binarization to the family feature in the Titanic dataset, we observed a minor improvement in model accuracy. This illustrates how feature engineering techniques like binarization can play a crucial role in the machine learning pipeline.

Remember, the effectiveness of binarization and other preprocessing steps can vary depending on the dataset and the specific problem at hand. It’s always important to experiment and evaluate different approaches to find the best solution for your specific use case.