MAPIE
FLAML
| MAPIE | FLAML | |
|---|---|---|
| 1 | 9 | |
| 1,155 | 3,682 | |
| 1.9% | 1.3% | |
| 9.7 | 7.9 | |
| 5 days ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | 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.
MAPIE
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How to calculate confidence level of a regression model?
Did some research and apparently there are number of ways and regression seems to be quite hard and no straightforward answer. Came across MAPIE (https://github.com/scikit-learn-contrib/MAPIE) which predicts a upper and lower range of values.. how can I then calculate the confidence for a given prediction?
FLAML
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AutoGen: Enabling Next-Gen GPT-X Applications
I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
4. FLAML
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
- [D] If there’s one practical tip you wish should have been drilled deeply into you when you first started out learning about deep learning, what would it be?
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what is the future of ML.NET?
Improved AutoML - Again, with collaboration from Microsoft Research, we used FLAML to update our existing AutoML solutions. What does this mean for you? You're using the latest techniques but all you need is a problem to solve and some data to get started.
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/FLAML/blob/main/notebook/flaml_automl.ipynb
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[N] Fast AutoML with Microsoft's FLAML + Ray Tune
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & seamlessly parallelizable algorithms across a cluster.
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[R] FLAML - Fast and Lightweight AutoML library
Looks nice but I wonder if this is practical for non-tiny problems. The papers are a bit hard to follow but it looks like training is restarted with every new architecture choice. As for the library itself, the only large neural net example is a finetune of an NLP model that only searches over ADAM's optimizer params - which could be useful but it's a stretch to call that AutoML.
- Flaml – Cost-effective hyperparameter optimization AutoML
What are some alternatives?
data-science-notes - Notes of IBM Data Science Professional Certificate Courses on Coursera
autogluon - Fast and Accurate ML in 3 Lines of Code
pycaret - An open-source, low-code machine learning library in Python
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
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.
PyABSA - Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
60-Days-of-Data-Science-and-ML - 60 Days of Data Science and ML
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
nyc_traffic_flask - Flask App with leaflet.js that can perform NYC Traffic Prediction
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.