visualizer2 | Audio Visualization in Rust | Data Visualization library

by Rahix Rust Version: Current License: GPL-3.0

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

kandi X-RAY | visualizer2 Summary

visualizer2 is a Rust library typically used in Analytics, Data Visualization applications. visualizer2 has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Audio Visualization in Rust

Support

Quality

Security

License

Reuse

Support

visualizer2 has a low active ecosystem.

It has 41 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of visualizer2 is current.

Quality

visualizer2 has no bugs reported.

Security

visualizer2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

visualizer2 is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

visualizer2 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.

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 visualizer2

Get all kandi verified functions for this library.

visualizer2 Key Features

No Key Features are available at this moment for visualizer2.

visualizer2 Examples and Code Snippets

No Code Snippets are available at this moment for visualizer2.

Community Discussions

Trending Discussions on visualizer2

How to get the predicted probabilities of a classification model?

QUESTION

How to get the predicted probabilities of a classification model?

Asked 2019-Mar-28 at 11:45

I'm trying out different classification models using a binary dependent variable (occupied/unoccupied). The models I am interested in are Logistic regression, Decision tree and Gaussian Naïve Bayes.

My input data is a csv-file with a datetime index (e.g. 2019-01-07 14:00), three variable columns ("R", "P", "C", containing numerical values), and the dependent variable column ("value", containing the binary values).

Training the model is not the problem, that all works fine. All the models give me their prediction in binary values (this of course should be the ultimate outcome), but I would also like to see the predicted probabilities which made them decide on either of the binary values. Is there any way to get also these values?

I have tried all of the classification visualizers that function with the yellowbrick package (ClassBalance, ROCAUC, ClassificationReport, ClassPredictionError). But all of these don't give me a graph that shows the calculated probabilities by the model for the data set.

...

ANSWER

Answered 2019-Mar-28 at 11:45

In most sklearn estimators (if not all) you have a method for obtaining the probability that precluded the classification, either in log probability or probability.

For example, if you have your Naive Bayes classifier and you want to obtain probabilities but not classification itself, you could do (I used same nomenclatures as in your code):

Source https://stackoverflow.com/questions/55395874

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

Vulnerabilities

No vulnerabilities reported

Install visualizer2

You can download it from GitHub.
Rust is installed and managed by the rustup tool. Rust has a 6-week rapid release process and supports a great number of platforms, so there are many builds of Rust available at any time. Please refer rust-lang.org for more information.

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: