pycaret
pyod
Our great sponsors
| pycaret | pyod | |
|---|---|---|
| 5 | 7 | |
| 8,406 | 7,941 | |
| 2.0% | - | |
| 9.4 | 7.7 | |
| 6 days ago | 8 days ago | |
| Jupyter Notebook | Python | |
| MIT License | BSD 2-clause "Simplified" License |
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.
pycaret
- pycaret: An open-source, low-code machine learning library in Python
- Predictive Maintenance and Anomaly Detection Resources
- Pycaret
-
How to look for help on data science?
Take a look at Pycaret python library. https://github.com/pycaret/pycaret
-
What is your DS stack? (and roast mine :) )
If you want to try pycaret exists, not sure how similar it is to caret, but it does all the steps in ML project. And Gluon for DL.
pyod
-
A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
-
Analyze defects and errors in the created images
PyOD
-
Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
-
[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
What are some alternatives?
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
tods - TODS: An Automated Time-series Outlier Detection System
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
alibi-detect - Algorithms for outlier, adversarial and drift detection
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
pymiere - Python for Premiere pro