modelStudio | 📍 Interactive Studio for Explanatory Model Analysis | Machine Learning library
kandi X-RAY | modelStudio Summary
kandi X-RAY | modelStudio Summary
modelStudio is a R library typically used in Artificial Intelligence, Machine Learning applications. modelStudio has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.
The modelStudio package automates the explanatory analysis of machine learning predictive models. It generates advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks (e.g. mlr/mlr3, xgboost, caret, h2o, parsnip, tidymodels, scikit-learn, lightgbm, keras/tensorflow). The main modelStudio() function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. Easily save the dashboard and share it with others. Tools for Explanatory Model Analysis unite with tools for Exploratory Data Analysis to give a broad overview of the model behavior. The modelStudio package is a part of the DrWhy.AI universe.
The modelStudio package automates the explanatory analysis of machine learning predictive models. It generates advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks (e.g. mlr/mlr3, xgboost, caret, h2o, parsnip, tidymodels, scikit-learn, lightgbm, keras/tensorflow). The main modelStudio() function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. Easily save the dashboard and share it with others. Tools for Explanatory Model Analysis unite with tools for Exploratory Data Analysis to give a broad overview of the model behavior. The modelStudio package is a part of the DrWhy.AI universe.
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Quality
Security
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Support
modelStudio has a low active ecosystem.
It has 283 star(s) with 32 fork(s). There are 15 watchers for this library.
It had no major release in the last 12 months.
There are 5 open issues and 81 have been closed. On average issues are closed in 12 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of modelStudio is v3.1.0
Quality
modelStudio has 0 bugs and 0 code smells.
Security
modelStudio has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
modelStudio code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
modelStudio 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.
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modelStudio releases are available to install and integrate.
Installation instructions are not available. Examples and code snippets are available.
It has 2371 lines of code, 0 functions and 19 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of modelStudio
modelStudio Key Features
No Key Features are available at this moment for modelStudio.
modelStudio Examples and Code Snippets
No Code Snippets are available at this moment for modelStudio.
Community Discussions
Trending Discussions on modelStudio
QUESTION
Partial dependence/ALE/ICE plots XgBoost in r
Asked 2021-Apr-16 at 10:23
I am trying to plot pdp, ale and ICE plots for a regression Xgboost model in r built using the Xgboost library. I have tried this using the pdp library:
...ANSWER
Answered 2021-Apr-16 at 10:23You need to specify the type. If ICP is continuous, try
p1xv <- partial(xgbc, pred.var = "za1", ice = TRUE, center = TRUE, plot = TRUE, rug = TRUE, alpha = 0.1, plot.engine = "ggplot2", train = xv, type = "regression")
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install modelStudio
You can download it from GitHub.
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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