fastknn | Fast k-Nearest Neighbors Classifier for Large Datasets | Machine Learning library

by davpinto R Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | fastknn Summary

fastknn is a R library typically used in Artificial Intelligence, Machine Learning applications. fastknn has no vulnerabilities and it has low support. However fastknn has 49 bugs. You can download it from GitHub.

FastKNN

Support

Quality

Security

License

Reuse

Support

fastknn has a low active ecosystem.

It has 60 star(s) with 17 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of fastknn is current.

Quality

fastknn has 49 bugs (0 blocker, 0 critical, 31 major, 18 minor) and 370 code smells.

Security

fastknn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fastknn code analysis shows 0 unresolved vulnerabilities.

There are 15 security hotspots that need review.

License

fastknn does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

fastknn releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

It has 2572 lines of code, 0 functions and 17 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of fastknn

Get all kandi verified functions for this library.

fastknn Key Features

No Key Features are available at this moment for fastknn.

fastknn Examples and Code Snippets

No Code Snippets are available at this moment for fastknn.

Community Discussions

Trending Discussions on fastknn

How to add weights to dataset variables using kNN in Octave?

QUESTION

How to add weights to dataset variables using kNN in Octave?

Asked 2017-Aug-20 at 11:35

I am implementing this kNN algorithm in Octave.

The function itself is declared like this

...

ANSWER

Answered 2017-Aug-20 at 11:35

To put more weight on one feature, you can multiply the given feature by a certain value.

This is equivalent to stretching the space along the respective dimension, effectively putting more emphasis on changes in the respective feature when the distances are calculated.

The fastKnn function that you mentioned uses the "mode" of the nearest k neighbors (i.e. the most frequent value). This is appropriate for classification (where you have a few classes that you want to predict), but not very useful for your situation.

For regression, taking the average of the values of the nearest neighbors is a good choice.

Source https://stackoverflow.com/questions/45755024

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install fastknn

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: