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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?
misc
- [P] New predictor does classification intermixed with regression
- New Algorithm Tops 34 Scikit-Learn Classifiers on the Titanic Dataset
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New Algorithm Tops 37 Scikit-Learn Classifiers on the Titanic Dataset
Regarding accuracy, occasionaly deodel outdoes more established algorithms like RandomForest, GradientBoostingClassifier, MLPClassifier, SVC, etc. Such an occasion is presented in here.
- Easy Machine Learning Dataset Evaluation Tool (Update)
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N\A cell in the matrix
deodel
- At what point do you consider dropping a categorical feature with missing values?
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The unclean data omnivore
csv_dataset_eval.ipynb
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Robust mixed attributes classifier (machine learning)
equal_width_vs_equal_frequency
- Estimating predictability of raw CSV files
- Classification with mixed attributes
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
nbviewer - nbconvert as a web service: Render Jupyter Notebooks as static web pages
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
grape - 🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
ydata-synthetic - Synthetic data generators for tabular and time-series data
general_class_balancer - Data matching algorithm for categorical and continuous variables
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
dcai-lab - Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 👩🏽💻
imbalanced-learn - A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
snorkel - A system for quickly generating training data with weak supervision