scikit-multiflow / streaming-datasets
A collection of data sets for stream learning.
☆32Updated 4 years ago
Related projects ⓘ
Alternatives and complementary repositories for streaming-datasets
- The stream-learn is an open-source Python library for difficult data stream analysis.☆62Updated 6 months ago
- Extra functionalities for river☆14Updated 6 months ago
- Python Meta-Feature Extractor package.☆126Updated 4 months ago
- CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system☆76Updated last year
- An automated machine learning tool aimed to facilitate AutoML research.☆96Updated 2 months ago
- Extended Complexity Library in R☆57Updated 3 years ago
- A collection of resources for concept drift data and software☆36Updated 9 years ago
- The Tornado framework, designed and implemented for adaptive online learning and data stream mining in Python.☆127Updated last year
- ☆71Updated last month
- Pipeline Profiler is a tool for visualizing machine learning pipelines generated by AutoML tools.☆84Updated last year
- Clustering stability analysis in Python with a scikit-learn compatible API.☆20Updated last year
- A real-time adaptive predictive system for evolving data streams. Inspired by MOA and scikit-multiflow, following Scikit's philosophy.☆8Updated 2 years ago
- scikit-mine : pattern mining in Python☆72Updated last year
- One of the ancestors of River☆33Updated 4 years ago
- Surrogate Assisted Feature Extraction☆36Updated 3 years ago
- A new framework to generate interpretable classification rules☆17Updated last year
- Learn2Clean: Optimizing the Sequence of Tasks for Data Preparation and Cleaning☆50Updated last year
- ☆47Updated 6 years ago
- Meta-Feature Extractor☆28Updated 2 years ago
- Born-Again Tree Ensembles: Transforms a random forest into a single, minimal-size, tree with exactly the same prediction function in the …☆64Updated last year
- ☆58Updated last week
- Feature Selection for Clustering☆94Updated 6 years ago
- Tutorial on how to use the SHAP library to explain the feature importance with Shapley values.☆19Updated 5 years ago
- ☆15Updated 5 years ago
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Fast K-Medoids clustering in Python with FasterPAM☆65Updated 2 months ago
- Fast and incremental explanations for online machine learning models. Works best with the river framework.☆51Updated last year
- ☆15Updated 3 years ago
- Algorithms for detecting changes from a data stream.☆116Updated 6 years ago
- Datasets for concept drift detection☆27Updated 7 years ago