benchmarks
pycaret
| benchmarks | pycaret | |
|---|---|---|
| 1 | 5 | |
| 163 | 8,406 | |
| 0.6% | 1.0% | |
| 4.4 | 9.4 | |
| 8 days ago | 7 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | 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.
benchmarks
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[D] [R] Hyperparameter space for lightGBM
A good starting point could be the CatBoost quality benchmarks, which also tune LightGBM. You can find their hyperparameter settings here.
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?
global-temp-change-animation - This animated map shows the change in surface temperature around the world from 1970 to 2021, based on data from Kaggle.
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.
Intrusion-Detection-System-Using-Machine-Learning - Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
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.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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.
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
tiler - GliGli's TileMotion video codec (data science / machine learning inspired; trivially simple to decode)