Machine-Learning-Algorithms | 自己总结的机器学习的算法与相应的程序

by huxiaoman7 Python Version: Current License: No License

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

kandi X-RAY | Machine-Learning-Algorithms Summary

Machine-Learning-Algorithms is a Python library. Machine-Learning-Algorithms has no bugs, it has no vulnerabilities and it has low support. However Machine-Learning-Algorithms build file is not available. You can download it from GitHub.

自己总结的机器学习的算法与相应的程序

Support

Quality

Security

License

Reuse

Support

Machine-Learning-Algorithms has a low active ecosystem.

It has 23 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Machine-Learning-Algorithms has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Machine-Learning-Algorithms is current.

Quality

Machine-Learning-Algorithms has 0 bugs and 0 code smells.

Security

Machine-Learning-Algorithms has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Machine-Learning-Algorithms code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Machine-Learning-Algorithms 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

Machine-Learning-Algorithms releases are not available. You will need to build from source code and install.

Machine-Learning-Algorithms has no build file. You will be need to create the build yourself to build the component from source.

It has 144 lines of code, 8 functions and 2 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed Machine-Learning-Algorithms and discovered the below as its top functions. This is intended to give you an instant insight into Machine-Learning-Algorithms implemented functionality, and help decide if they suit your requirements.

Searches for the nearest neighbors .
Creates a larger model .
Create a tree from data set .
Visualize training .
Initialize the object .
Print pre order information .

Get all kandi verified functions for this library.

Machine-Learning-Algorithms Key Features

No Key Features are available at this moment for Machine-Learning-Algorithms.

Machine-Learning-Algorithms Examples and Code Snippets

No Code Snippets are available at this moment for Machine-Learning-Algorithms.

Community Discussions

Trending Discussions on Machine-Learning-Algorithms

How to calculate 95% CI for accuracy and kappa in caret

QUESTION

How to calculate 95% CI for accuracy and kappa in caret

Asked 2021-Mar-05 at 10:44

I am running k-fold repeated training with the caret package and would like to calculate the confidence interval for my accuracy metrics. This tutorial prints a caret training object that shows accuracy/kappa metrics and associated SD: https://machinelearningmastery.com/tune-machine-learning-algorithms-in-r/. However, when I do this, all that is listed are the metric average values.

...

ANSWER

Answered 2021-Mar-01 at 07:44

It looks like it is stored in the results variable of the resultant object.

Source https://stackoverflow.com/questions/66416014

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

Vulnerabilities

No vulnerabilities reported

Install Machine-Learning-Algorithms

You can download it from GitHub.
You can use Machine-Learning-Algorithms like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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 .

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