pycaret
anomaly-detection-resources
| pycaret | anomaly-detection-resources | |
|---|---|---|
| 5 | 98 | |
| 8,428 | 7,887 | |
| 1.2% | - | |
| 9.4 | 4.6 | |
| 8 days ago | 13 days ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU Affero General Public License v3.0 |
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.
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.
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
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.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
loglizer - A machine learning toolkit for log-based anomaly detection [ISSRE'16]
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
DGFraud - A Deep 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.
UGFraud - An Unsupervised Graph-based Toolbox for Fraud Detection