Classification: K-nearest neighbor

Classification is a supervised machine learning method that predicts the correct label of input data. The model is trained using training data, and then evaluated on test data. The model is then used to perform predictions on new, unseen data.

K-nearest neighbors (KNN) is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It works by finding the k most similar examples in the training data to a new data point, and then using those examples to make a prediction about the new data point.

Classification: In classification, the KNN algorithm assigns a new data point to the class that is most common among its k nearest neighbors. For example, if we are using KNN to classify images of cats and dogs, and we have a new image that is most similar to 3 cat images and 2 dog images, then the KNN algorithm would classify the new image as a cat.

 


Advantages of KNN:
  • KNN is a Simple to understand and implement.
  • Doesn't require a complex training phase.
  • KNN can be used for both classification and regression tasks. 

Disadvantages of KNN:

  • KNN can be computationally expensive, especially for large datasets.
  • KNN is sensitive to noise and outliers in the data.
  • KNN is a lazy learning algorithm, which means that it does not learn a model from the training data. Instead, it simply stores the training data and uses it to make predictions on new data points.

 

How to choose the value of k:

·        The value of k is a hyperparameter of the KNN algorithm, which means that it needs to be set by the user before the algorithm can be trained. The optimal value of k will vary depending on the dataset, but it is generally recommended to start with a small value (e.g., k = 1 or k = 3) and then increase it until the performance of the algorithm improves.