deodel
snorkel
Our great sponsors
| deodel | snorkel | |
|---|---|---|
| 13 | 5 | |
| 5 | 5,707 | |
| - | 0.8% | |
| 6.3 | 5.5 | |
| 2 months ago | 2 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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.
deodel
- [P] New predictor does classification intermixed with regression
- Easy Machine Learning Dataset Evaluation Tool (Update)
-
What are some practical tips for efficiently handling missing or null values in datasets during data analysis in Python?
You could use this new classifier deodel that is very robust. It deals seamlessly with missing data, nulls, mixed numerical and categorical attributes, and multi-class targets. You can see an application with this tool:
-
What’s your approach to highly imbalanced data sets?
Just to mention that there is also a new algorithm that is immune to the imbalance of data. An implementation in python is available at: - https://github.com/c4pub/deodel
- Robust mixed attributes classifier (machine learning)
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The deodel classifier can act as a quick dataset evaluation tool. If your data is available in table format, you can check its potential for prediction/classification. Just feed it to deodel. It accepts mixed attributes without any preliminary curation. It simply considers attribute values expressed as floats (dot decimal) as being continuous. It accepts even a mix of continuous and categorical values for the same attribute column.
- [D] Open-source package to mix numerical, categorical and text features?
- [P] Discretization: equal-width trumps equal-frequency?
- [P] Discretization: equal-width beats equal-frequency?
snorkel
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
-
Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
-
[Discussion] Methods for enhancing high-quality dataset A with low-quality dataset
Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
ydata-synthetic - Synthetic data generators for tabular and time-series data
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
misc
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
general_class_balancer - Data matching algorithm for categorical and continuous variables
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]