K-Nearest Neighbors | Panda AI Studio

K-Nearest Neighbors (KNN)

AI Terms

K-Nearest Neighbors (KNN)

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:

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.
What are the advantages of using KNN?
- KNN is easy to understand and implement, and it can be effective for simple problems.
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.

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