Scikit Learn

Scikit-learn is a widely used Python library for machine learning, providing tools for various tasks including model building, data preprocessing, and model evaluation. Current research focuses on enhancing its capabilities through extensions that address challenges like imbalanced datasets, hierarchical classification, and efficient handling of large datasets, often integrating with other libraries like PyTorch and TensorFlow. This readily accessible and versatile toolkit significantly impacts the machine learning community by streamlining research and development, facilitating reproducible results, and enabling broader application of machine learning techniques across diverse fields.

Papers

March 29, 2022

Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping
Lars Reimann, Günter Kniesel-Wünsche
Machine Learning Scikit Learn Kaggle Competition Commercial ML New Wrapper Method

March 16, 2022

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach
Machine Learning Model Raw Data Level Test Easy to Use Library Scikit Learn

December 13, 2021

HiClass: a Python library for local hierarchical classification compatible with scikit-learn
Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard
Python Library True Class Hierarchical Learning Hierarchical Classification Scikit Learn

November 24, 2021

IMBENS: Ensemble Class-imbalanced Learning in Python
Zhining Liu, Jian Kang, Hanghang Tong, Yi Chang
Ensemble Learning Class Imbalance Ensemble Method Scikit Learn Class Imbalanced Learning

November 19, 2021

Data imputation and comparison of custom ensemble models with existing libraries like XGBoost, Scikit learn, etc. for Predictive Equipment failure
Tejas Y. Deo
Data Set Ensemble Model XGBoost Model Data Imputation Scikit Learn Failure Prediction Imputation Model

Scikit Learn

Papers

Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data

HiClass: a Python library for local hierarchical classification compatible with scikit-learn

IMBENS: Ensemble Class-imbalanced Learning in Python

Data imputation and comparison of custom ensemble models with existing libraries like XGBoost, Scikit learn, etc. for Predictive Equipment failure