Python Machine Learning: Binary Classification of MNIST Data with Scikit-learn in Python

Machine learning is a rapidly growing field in the tech industry, and Python has become the go-to language for many data scientists and machine learning engineers. In this article, we will explore how to perform binary classification of MNIST data using Scikit-learn in Python.

What is MNIST data?

The MNIST dataset is a collection of handwritten digits that is commonly used for training and testing machine learning models. Each image in the dataset is a grayscale 28×28 pixel image of a handwritten digit from 0 to 9. The goal of the dataset is to correctly classify each digit into its corresponding category.

Binary Classification

In binary classification, we are tasked with classifying data into one of two categories. For our example, we will focus on classifying the handwritten digits into either “0” or “1”.

Using Scikit-learn

Scikit-learn is a powerful machine learning library in Python that provides tools for building and training machine learning models. We can use the library to build a simple linear support vector machine (SVM) classifier to perform binary classification on the MNIST dataset.

    
      # Import necessary libraries
      import numpy as np
      from sklearn import datasets, svm

      # Load the MNIST dataset
      digits = datasets.load_digits()

      # Create binary classification labels for "0" and "1"
      binary_labels = np.where(digits.target < 2, 1, 0)

      # Create an SVM classifier
      clf = svm.SVC(gamma=0.001)

      # Fit the classifier to the data
      clf.fit(digits.data, binary_labels)

      # Make predictions
      predicted = clf.predict(digits.data)

      # Calculate accuracy
      accuracy = np.mean(predicted == binary_labels)
      print(f"Accuracy: {accuracy}")
    
  

By running this code snippet, we can train an SVM classifier on the MNIST dataset and calculate the accuracy of our predictions. This is a simple example of how we can perform binary classification using Scikit-learn in Python.

Conclusion

Python is a powerful language for machine learning, and Scikit-learn provides a user-friendly interface for building and training machine learning models. In this article, we explored how to perform binary classification of MNIST data using Scikit-learn in Python. By utilizing the tools and libraries available in Python, we can easily build machine learning models to classify and analyze data.