deepchecks
shapash
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deepchecks | shapash | |
---|---|---|
15 | 8 | |
3,350 | 2,642 | |
3.2% | 1.3% | |
8.2 | 8.6 | |
10 days ago | about 1 month ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deepchecks
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Detect, Defend, Prevail: Payments Fraud Detection using ML & Deepchecks
Also if you have any confusion related to it. You can directly go to their discussion section in github :
- Deepchecks: Open-source ML testing and validation library
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Deepchecks' New Open Source is on Product Hunt, and Needs Your Help
GitHub for Deepchecks: https://github.com/deepchecks/deepchecks
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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Data Validation tools
I use DeepChecks for my continuous training pipelines. You can check out the Data Integrity Checks.
- Deepchecks
- deepchecks: Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
- QA help comes in many forms: Sometimes, from your heavily funded competitor
- Deepchecks: An open-source tool for testing machine learning models and data
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Test suites for machine learning models in Python (New OSS package)
And if you liked the project, we'll be delighted to count you as one of our stargazers at https://github.com/deepchecks/deepchecks/stargazers!
shapash
- GitHub - MAIF/shapash: Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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This A.I.-generated artwork, Théùtre D'opéra Spatial, won first place at an art competition, and the art community isn't happy about it
There's work being done in that regard (like this python module), but as far as I know it's very clearly statistical guesstimates, and though it "works", the mathematical foundations are still somewhat shaky. There are heuristics in there we can't get rid of for now. But it's still better than nothing. Waaaaaay better than nothing.
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Hacker News top posts: Jun 14, 2022
Shapash â Python library to make machine learning interpretable\ (4 comments)
- Shapash â Python library to make machine learning interpretable
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State of the Art data drift libraries on Python?
Try out eurybia, from the author of shapash which is a brilliant library as well.
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[P] It Is Now Possible To Generate a Model Audit Report with Shapash
With the new version of Shapash that is now available, you can document each model you release into production. Within a few lines of code, you can include in an HTML report all the information about your model (and its associated performance), the data it uses, its learning strategy, ⊠this report is designed to be easily shared with a Data Protection Officer, an internal audit department, a risk control department, a compliance department, or anyone who wants to understand his work.
- [D] Has anyone ever used the SHAP and LIME models in machine learning?
What are some alternatives?
great_expectations - Always know what to expect from your data.
shap - A game theoretic approach to explain the output of any machine learning model.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
interpret - Fit interpretable models. Explain blackbox machine learning.
model-validation-toolkit - Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
feast - Feature Store for Machine Learning
GlassCode - This plugin allows you to make JetBrains IDEs to be fully transparent while keeping the code sharp and bright.
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
trulens - Evaluation and Tracking for LLM Experiments
giskard - đą Open-Source Evaluation & Testing framework for LLMs and ML models
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms