Iris-flower-classification | Simple Classification program to predict | Machine Learning library
kandi X-RAY | Iris-flower-classification Summary
kandi X-RAY | Iris-flower-classification Summary
Iris-flower-classification is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow, Keras applications. Iris-flower-classification has no bugs, it has no vulnerabilities and it has low support. However Iris-flower-classification build file is not available. You can download it from GitHub.
Simple Classification program to predict the species of an iris flower.
Simple Classification program to predict the species of an iris flower.
Support
Quality
Security
License
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Support
Iris-flower-classification has a low active ecosystem.
It has 6 star(s) with 6 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 49 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Iris-flower-classification is current.
Quality
Iris-flower-classification has 0 bugs and 0 code smells.
Security
Iris-flower-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Iris-flower-classification code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Iris-flower-classification 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.
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Iris-flower-classification releases are not available. You will need to build from source code and install.
Iris-flower-classification has no build file. You will be need to create the build yourself to build the component from source.
It has 62 lines of code, 5 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 Iris-flower-classification and discovered the below as its top functions. This is intended to give you an instant insight into Iris-flower-classification implemented functionality, and help decide if they suit your requirements.
- Predict the closest label
- Find the closest row in the given row
- Euclidean distance between a and b
- Decode numbers
- Fit the model
Get all kandi verified functions for this library.
Iris-flower-classification Key Features
No Key Features are available at this moment for Iris-flower-classification.
Iris-flower-classification Examples and Code Snippets
No Code Snippets are available at this moment for Iris-flower-classification.
Community Discussions
Trending Discussions on Iris-flower-classification
QUESTION
Spring Cloud Data Flow HTTP source App throws ObjectPostProcessor required error
Asked 2018-Aug-27 at 02:17
I am trying to deploy a simple http stream app but it fails to deploy with the message :
...ANSWER
Answered 2018-Aug-27 at 02:17We are tracking an anomaly related to Spring Boot's default security behavior change via spring-cloud-stream-app-starters#19.
Until we release a new version of the App, you can disable the security config by deploying the stream with the following deployment property:
stream deploy .... --properties app.http.spring.autoconfigure.exclude=
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Iris-flower-classification
You can download it from GitHub.
You can use Iris-flower-classification 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.
You can use Iris-flower-classification 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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