TABLE OF CONTENTS:
The K-Nearest Neighbors (KNN) algorithm is a simple yet powerful classification and regression algorithm that is widely used in machine learning. It is a non-parametric algorithm that does not make any assumptions about the underlying data distribution. In this article, we will explore the KNN algorithm in detail and provide a step-by-step guide on how to implement it in Python.
The KNN algorithm is a type of instance-based learning, which means that it makes predictions based on the data instances or examples that are closest to a new data point. The KNN algorithm is called the K-Nearest Neighbors algorithm because it considers the K closest neighbors to the new data point when making a predictions the average of the K nearest neighbors’ target values as the predicted value for the new data point.
Click on image of see full width
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checkbox-analytics | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". |
cookielawinfo-checkbox-functional | 11 months | The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". |
cookielawinfo-checkbox-others | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. |
cookielawinfo-checkbox-performance | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". |
viewed_cookie_policy | 11 months | The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. |