explainerdashboard | Quickly build Explainable AI dashboards that show the inner | Data Visualization library
kandi X-RAY | explainerdashboard Summary
kandi X-RAY | explainerdashboard Summary
explainerdashboard is a Python library typically used in Analytics, Data Visualization, Jupyter applications. explainerdashboard has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install explainerdashboard' or download it from GitHub, PyPI.
This package makes it convenient to quickly deploy a dashboard web app that explains the workings of a (scikit-learn compatible) machine learning model. The dashboard provides interactive plots on model performance, feature importances, feature contributions to individual predictions, "what if" analysis, partial dependence plots, SHAP (interaction) values, visualisation of individual decision trees, etc. You can also interactively explore components of the dashboard in a notebook/colab environment (or just launch a dashboard straight from there). Or design a dashboard with your own custom layout and explanations (thanks to the modular design of the library). And you can combine multiple dashboards into a single ExplainerHub.
This package makes it convenient to quickly deploy a dashboard web app that explains the workings of a (scikit-learn compatible) machine learning model. The dashboard provides interactive plots on model performance, feature importances, feature contributions to individual predictions, "what if" analysis, partial dependence plots, SHAP (interaction) values, visualisation of individual decision trees, etc. You can also interactively explore components of the dashboard in a notebook/colab environment (or just launch a dashboard straight from there). Or design a dashboard with your own custom layout and explanations (thanks to the modular design of the library). And you can combine multiple dashboards into a single ExplainerHub.
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Quality
Security
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Support
explainerdashboard has a medium active ecosystem.
It has 1834 star(s) with 245 fork(s). There are 22 watchers for this library.
There were 4 major release(s) in the last 6 months.
There are 17 open issues and 191 have been closed. On average issues are closed in 57 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of explainerdashboard is 0.4.7
Quality
explainerdashboard has no bugs reported.
Security
explainerdashboard has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
explainerdashboard is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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explainerdashboard releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed explainerdashboard and discovered the below as its top functions. This is intended to give you an instant insight into explainerdashboard implemented functionality, and help decide if they suit your requirements.
- Returns a shap
- Return True if obj is an instance of type
- Add flask routes
- Create anExplainer from a config file
- Convert a list of YAML tuples to a function
- Decodes callables
- Start the Explainer dashboard
- Dump the dashboard configuration to yaml
- Get local ip address
- Callback called when a contribution is selected
- Display an explanation of the dashboard dashboard
- Save the dashboard configuration to a yaml file
- Returns a random random index
- Callback called when the interaction is done
- Store the parameters of the function
- Set shap values
- Callback for callback
- Callback invoked when the component is called
- Returns a pandas DataFrame containing row data
- Layout of the feature
- Hash logins
- Splits a pipeline
- Create an Explainer from a configuration file
- Callback when the component is called
- Instantiate dashboard instances
- Plot the trees at the given index
Get all kandi verified functions for this library.
explainerdashboard Key Features
No Key Features are available at this moment for explainerdashboard.
explainerdashboard Examples and Code Snippets
Run ExplainerDashboard inside a streamlit application
Python
Lines of Code : 9License : Strong Copyleft (CC BY-SA 4.0)
Lines of Code : 9License : Strong Copyleft (CC BY-SA 4.0)
Copydef app():
"""
Set appearance to wide mode.
"""
st.title("This is the machine learning page")
dashboardurl = 'http://127.0.0.1:8050/'
st.components.v1.iframe(dashboardurl, width=None, height=900, scrolling=True)
Community Discussions
Trending Discussions on explainerdashboard
QUESTION
Run ExplainerDashboard inside a streamlit application
Asked 2022-Mar-28 at 03:05
I want to run the ExplainerDashboard inside a Streamlit application. Is there a way I can do that? I have tried all modes of ExplainerDashboard run() function but it still isn't working for me.
Here is what I have done so far but it doesn't work.
...ANSWER
Answered 2022-Mar-26 at 01:44Run the dashboard normally, and run streamlit loading the url of dashboard as iframe in streamlit app.
- run dashboard
- Get dashboard local url
- run streamlit
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install explainerdashboard
You can install the package through pip:.
Support
Documentation can be found at explainerdashboard.readthedocs.io. Example notebook on how to launch dashboards for different model types here: dashboard_examples.ipynb. Example notebook on how to interact with the explainer object here: explainer_examples.ipynb. Example notebook on how to design a custom dashboard: custom_examples.ipynb.
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