rhiever / Sklearn Benchmarks
Licence: mit
A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.
Stars: ✭ 194
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Sklearn Benchmarks
Ml Workspace
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Stars: ✭ 2,337 (+1104.64%)
Mutual labels: jupyter-notebook, scikit-learn
Cheatsheets.pdf
📚 Various cheatsheets in PDF
Stars: ✭ 159 (-18.04%)
Mutual labels: jupyter-notebook, scikit-learn
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron
Stars: ✭ 151 (-22.16%)
Mutual labels: jupyter-notebook, scikit-learn
Py4chemoinformatics
Python for chemoinformatics
Stars: ✭ 140 (-27.84%)
Mutual labels: jupyter-notebook, scikit-learn
Practical Machine Learning With Python
Machine Learning Tutorials in Python
Stars: ✭ 183 (-5.67%)
Mutual labels: jupyter-notebook, scikit-learn
Ml Forex Prediction
Predicting Forex Future Price with Machine Learning
Stars: ✭ 142 (-26.8%)
Mutual labels: jupyter-notebook, scikit-learn
Python Clustering Exercises
Jupyter Notebook exercises for k-means clustering with Python 3 and scikit-learn
Stars: ✭ 153 (-21.13%)
Mutual labels: jupyter-notebook, scikit-learn
Pydata Chicago2016 Ml Tutorial
Machine learning with scikit-learn tutorial at PyData Chicago 2016
Stars: ✭ 128 (-34.02%)
Mutual labels: jupyter-notebook, scikit-learn
Bert Sklearn
a sklearn wrapper for Google's BERT model
Stars: ✭ 182 (-6.19%)
Mutual labels: jupyter-notebook, scikit-learn
Python Machine Learning Book 3rd Edition
The "Python Machine Learning (3rd edition)" book code repository
Stars: ✭ 2,883 (+1386.08%)
Mutual labels: jupyter-notebook, scikit-learn
Practical Machine Learning With Python
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
Stars: ✭ 1,868 (+862.89%)
Mutual labels: jupyter-notebook, scikit-learn
Pqkmeans
Fast and memory-efficient clustering
Stars: ✭ 189 (-2.58%)
Mutual labels: jupyter-notebook, scikit-learn
Interactive machine learning
IPython widgets, interactive plots, interactive machine learning
Stars: ✭ 140 (-27.84%)
Mutual labels: jupyter-notebook, scikit-learn
Python Machine Learning Book
The "Python Machine Learning (1st edition)" book code repository and info resource
Stars: ✭ 11,428 (+5790.72%)
Mutual labels: jupyter-notebook, scikit-learn
Hep ml
Machine Learning for High Energy Physics.
Stars: ✭ 133 (-31.44%)
Mutual labels: jupyter-notebook, scikit-learn
Machine Learning With Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Stars: ✭ 2,197 (+1032.47%)
Mutual labels: jupyter-notebook, scikit-learn
Pbpython
Code, Notebooks and Examples from Practical Business Python
Stars: ✭ 1,724 (+788.66%)
Mutual labels: jupyter-notebook, scikit-learn
Dive Into Machine Learning
Dive into Machine Learning with Python Jupyter notebook and scikit-learn! First posted in 2016, maintained as of 2021. Pull requests welcome.
Stars: ✭ 10,810 (+5472.16%)
Mutual labels: jupyter-notebook, scikit-learn
Machine Learning And Reinforcement Learning In Finance
Machine Learning and Reinforcement Learning in Finance New York University Tandon School of Engineering
Stars: ✭ 173 (-10.82%)
Mutual labels: jupyter-notebook, scikit-learn
Virgilio
Virgilio is developed and maintained by these awesome people.
You can email us virgilio.datascience (at) gmail.com or join the Discord chat.
Stars: ✭ 13,200 (+6704.12%)
Mutual labels: jupyter-notebook, scikit-learn
scikit-learn benchmarks
A centralized repository to report scikit-learn model performance across a variety of parameter settings and datasets.
Downloading the benchmark data
Please refer to PMLB to gain access to the curated datasets from this study. PMLB provides an easy-to-use Python interface to download the datasets.
Contributing
We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension of this project, please file a new issue so we can discuss it. Make sure to review our contribution guidelines before starting any work on this project.
Citing
If you use any of the code, data, or results from this project, please cite the following paper.
Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore (2017). Data-driven Advice for Applying Machine Learning to Bioinformatics Problems. arXiv e-print
BibTeX entry:
@misc{OlsonLaCava2017,
author={Olson, Randal S. and La Cava, William and Mustahsan, Zairah and Varik, Akshay and Moore, Jason H.},
title = {Data-driven Advice for Applying Machine Learning to Bioinformatics Problems},
year = {2017},
howpublished = {arXiv e-print. https://arxiv.org/abs/1708.05070},
}
Support for this project
This project was developed in the Computational Genetics Lab with funding from the NIH. We're incredibly grateful for their support during the development of this project!
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].