deodel
dcai-lab
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| deodel | dcai-lab | |
|---|---|---|
| 13 | 10 | |
| 5 | 397 | |
| - | 3.8% | |
| 6.3 | 5.4 | |
| 2 months ago | 4 months ago | |
| Python | Jupyter Notebook | |
| - | GNU Affero General Public License v3.0 |
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deodel
- [P] New predictor does classification intermixed with regression
- Easy Machine Learning Dataset Evaluation Tool (Update)
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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:
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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)
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[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?
dcai-lab
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Resources to learn practical/industry-focused ML (preferably using TensorFlow)?
Data-Centric AI honestly if you've been working on ML pipelines this might be familiar to you
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Andrew NG, github courses
Another great resource inspired by the Andrew Ng data-centric AI movement is the Introduction to Data-Centric AI course taught this past semester at MIT by PhDs.
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Good Beginner Courses for ML?
Data-centric AI course. Brand new, taught the 1st time a few months ago by MIT PhD grads. This covers how to ensure good data quality for your models. More data science havy.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Thanks for the kind words! Make sure to check out the current open MIT course if you are just starting out: https://dcai.csail.mit.edu/
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The Missing Semester of Your CS Education
Introduction to Data-Centric AI https://dcai.csail.mit.edu
- Introduction to Data-Centric AI
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MIT Introduction to Data-Centric AI
Course homepage | Lecture videos on YouTube | Lab Assignments
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
llm-course - Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
snorkel - A system for quickly generating training data with weak supervision
ydata-synthetic - Synthetic data generators for tabular and time-series data
chordviz - A convolutional neural network trained using PyTorch to predict the next chord (as tablature) on a guitar based on image data. Includes labeling software for the image data as well as an iOS app for hosting and running the model.
misc
general_class_balancer - Data matching algorithm for categorical and continuous variables
UBB-INFO - All projects from university.