Python | KNN Regression aka KNeighborsRegressor | scikitlearn

K-nearest neighbors (KNN) regression, also known as KNeighborsRegressor, is a popular machine learning technique used for regression tasks in Python. When it comes to implementing KNN regression in Python, scikit-learn is one of the most widely used libraries.

What is K-nearest neighbors (KNN) regression?

K-nearest neighbors (KNN) regression is a type of supervised learning algorithm that can be used for both classification and regression tasks. In KNN regression, the output value for a new data point is predicted based on the average of the values of its k nearest neighbors.

Implementing KNN regression with scikit-learn

Scikit-learn is a powerful machine learning library for Python that provides various tools for implementing KNN regression. The KNeighborsRegressor class in scikit-learn is used to create a KNN regression model. It allows you to specify the number of neighbors (k) and other parameters to customize the model.

Example of KNN regression with scikit-learn

    
      from sklearn.neighbors import KNeighborsRegressor
      import numpy as np

      # Generate some random data for demonstration
      X_train = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
      y_train = np.array([10, 20, 30])
      X_test = np.array([[2, 3, 4]])

      # Create a KNN regression model
      knn_regressor = KNeighborsRegressor(n_neighbors=2)
      knn_regressor.fit(X_train, y_train)

      # Make predictions
      predictions = knn_regressor.predict(X_test)
      print(predictions)
    
  

Conclusion

K-nearest neighbors (KNN) regression, also known as KNeighborsRegressor, is a simple yet powerful algorithm for regression tasks in Python. With the help of scikit-learn, implementing and using KNN regression models is straightforward and effective. If you are working on a regression problem in Python, consider giving KNN regression a try!