Definition: K-nearest neighbors (KNN) is a supervised machine learning algorithm used for classification and regression tasks.

Detailed Description:

KNN works by assigning a data point to the class of its K nearest neighbors. The value of K is a hyperparameter that determines the number of neighbors considered.

KNN is a simple and intuitive algorithm, but it can be computationally expensive for large datasets. It is also sensitive to the choice of distance metric and the value of K.

Related Terms:

• Machine Learning
• Supervised Learning
• Classification
• Regression
• Distance Metric

FAQs:

1. How does KNN work?

   • KNN calculates the distance between a new data point and all training examples. The K nearest neighbors are identified, and the class of the majority of these neighbors is assigned to the new data point.

2. What are the advantages of using KNN?

   • KNN is easy to understand and implement, and it can be effective for simple problems.

3. What are the disadvantages of using KNN?

   • KNN can be computationally expensive for large datasets, and it can be sensitive to the choice of distance metric and the value of K.