ml-benchmarks | Benchmarks for various machine learning packages | Machine Learning library

by scikit-learn Python Version: Current License: No License

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

kandi X-RAY | ml-benchmarks Summary

ml-benchmarks is a Python library typically used in Artificial Intelligence, Machine Learning applications. ml-benchmarks has no bugs, it has no vulnerabilities and it has low support. However ml-benchmarks build file is not available. You can download it from GitHub.

Benchmarks for various machine learning packages

Support

Quality

Security

License

Reuse

Support

ml-benchmarks has a low active ecosystem.

It has 85 star(s) with 45 fork(s). There are 43 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 3 have been closed. On average issues are closed in 23 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ml-benchmarks is current.

Quality

ml-benchmarks has 0 bugs and 0 code smells.

Security

ml-benchmarks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ml-benchmarks code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ml-benchmarks 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

ml-benchmarks releases are not available. You will need to build from source code and install.

ml-benchmarks has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Benchmark example .
Load training data .
Benchmark a function
Compute the inertia of the given centers .
Benchmark test .
Benchmark example
Return the explained variance .
Benchmark KL divergence .
Benchmark for MLpy
Benchmark method .

Get all kandi verified functions for this library.

ml-benchmarks Key Features

No Key Features are available at this moment for ml-benchmarks.

ml-benchmarks Examples and Code Snippets

No Code Snippets are available at this moment for ml-benchmarks.

Community Discussions

Trending Discussions on ml-benchmarks

clustering large data set using dask

QUESTION

clustering large data set using dask

Asked 2019-May-18 at 15:21

I ve installed dask. My main aim is clustering a large dataset, but before starting work on it, I want to make a few tests. However, whenever I want to run a dask code piece, it takes too much time and a memory error appears at the end. I tried their Spectral Clustering Example and the short code below.

Do you think what is the problem?

...

ANSWER

Answered 2019-May-18 at 15:21

The Scikit-Learn algorithms are not designed to train over large datasets. They are designed to operate on data that fits in memory. This is described here: https://ml.dask.org/#parallelize-scikit-learn-directly

Projects like Dask ML do have other algorithms that look like Scikit-Learn, but are implemented differently that do support larger dataset sizes. If you're looking for clustering then you might be interested in this page to see what is currently supported: https://ml.dask.org/clustering.html

Source https://stackoverflow.com/questions/56062523

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

Vulnerabilities

No vulnerabilities reported

Install ml-benchmarks

You can download it from GitHub.
You can use ml-benchmarks 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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