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Binarizer

A Binarizer in machine learning is a preprocessing technique used to convert continuous numerical data into binary values (0s and 1s) based on a threshold. This is often used in classification problems, feature engineering, or when dealing with categorical variables.

Using Binarizer in Scikit-learn​

Scikit-learn provides a Binarizer class that allows you to easily transform numerical data into binary form.

Example Usage​

from sklearn.preprocessing import Binarizer
import numpy as np

# Sample data
data = np.array([[1.5, -2.3, 3.2],
                 [0.8, -0.5, 1.2],
                 [2.4, 3.5, -1.1]])

# Define the Binarizer with a threshold (e.g., 0.5)
binarizer = Binarizer(threshold=0.5)

# Transform the data
binary_data = binarizer.fit_transform(data)

print(binary_data)

Output​

[[1. 0. 1.]
 [1. 0. 1.]
 [1. 1. 0.]]

Key Points​

  • Values greater than the threshold are converted to 1, otherwise 0.
  • Default threshold is 0.0 (anything above 0 becomes 1).
  • Useful in feature selection or when dealing with binary classification tasks.
Citation

Understanding Binarization in Data Preprocessing​

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.

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https://medium.com/@noorfatimaafzalbutt/understanding-binarization-in-data-preprocessing-663219320c6e