Machine-Learning-From-Scratch | 常用机器学习的算法简洁实现

by RRdmlearning Python Version: Current License: No License

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

kandi X-RAY | Machine-Learning-From-Scratch Summary

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

常用机器学习的算法简洁实现

Support

Quality

Security

License

Reuse

Support

Machine-Learning-From-Scratch has a low active ecosystem.

It has 625 star(s) with 303 fork(s). There are 18 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 1 have been closed. On average issues are closed in 301 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Machine-Learning-From-Scratch is current.

Quality

Machine-Learning-From-Scratch has 0 bugs and 140 code smells.

Security

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

Machine-Learning-From-Scratch code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

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

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

Machine-Learning-From-Scratch saves you 525 person hours of effort in developing the same functionality from scratch.

It has 1231 lines of code, 131 functions and 25 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-From-Scratch and discovered the below as its top functions. This is intended to give you an instant insight into Machine-Learning-From-Scratch implemented functionality, and help decide if they suit your requirements.

Plots the class distribution
Transforms the covariance matrix
Calculate the covariance matrix
Runs the classification
Compute the covariance matrix
Split train and test data
Shuffle data
Normalize X
Find k - fold cross validation sets
Fit the loss function
Fit the tree
Get bootstrap data
Transform the covariance matrix
Predict the n_estimator
Calculate the gradient of the tree
Fit the model
Fit the estimator
Sets the model parameters
Split training and test data
Calculate the variance reduction
Predict the class for the model
Calculate the correlation matrix
Calculate the weight of the classification
Standardize the data
Plots the image in 3d
Calculates the approximate update estimate
Calculate information gain

Get all kandi verified functions for this library.

Machine-Learning-From-Scratch Key Features

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

Machine-Learning-From-Scratch Examples and Code Snippets

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

Community Discussions

No Community Discussions are available at this moment for Machine-Learning-From-Scratch.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install Machine-Learning-From-Scratch

You can download it from GitHub.
You can use Machine-Learning-From-Scratch 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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