anomaly-detection-resources
pycaret
| anomaly-detection-resources | pycaret | |
|---|---|---|
| 98 | 5 | |
| 7,887 | 8,428 | |
| - | 1.2% | |
| 4.6 | 9.4 | |
| 13 days ago | 8 days ago | |
| Python | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | MIT 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.
anomaly-detection-resources
- anomaly-detection-resources: NEW Extended Research - star count:7507.0
- anomaly-detection-resources: NEW Extended Research - star count:7323.0
- anomaly-detection-resources: NEW Extended Research - star count:7109.0
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Time-based splitting performing significantly worse than random splitting
https://github.com/yzhao062/anomaly-detection-resources https://search.brave.com/search?q=imbalanced+dataset+machine+learning+github&source=desktop
pycaret
- pycaret: An open-source, low-code machine learning library in Python
- Predictive Maintenance and Anomaly Detection Resources
- Pycaret
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How to look for help on data science?
Take a look at Pycaret python library. https://github.com/pycaret/pycaret
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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.
What are some alternatives?
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
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.
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
loglizer - A machine learning toolkit for log-based anomaly detection [ISSRE'16]
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
DGFraud - A Deep Graph-based Toolbox for Fraud Detection
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
UGFraud - An Unsupervised Graph-based Toolbox for Fraud Detection
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.