Sklearn Compatible Python Packages

arm-mango

Parallel Hyperparameter Tuning in Python

polyfit

Scikit learn compatible constrained and robust polynomial regression in Python

xaisuite

XAISuite: Training and Explanation Generation Utilities for Machine Learning Models

onehot

One-Hot encoder with sklearn-ish API interface that process mixed string and numeric labels directly.

constrainedlr

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

panelsplit

A tool for panel data analysis.

pyoperon

Python bindings and scikit-learn interface for the Operon library for symbolic regression.

pyadlml

Sklearn flavored library containing numerous Activity of Daily Livings datasets, preprocessing methods, visualizations and models.

boost-loss

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost

sklearn-utilities

Utilities for scikit-learn. Append prediction to x, append prediction to x single, append x prediction to x, compose var estimator, data frame wrapper, drop by noise prediction, drop missing rows y, dummy regressor var, estimator wrapper base, excluded column transformer pandas, feature union pandas, id transformer, included column transformer pand

lightgbm-callbacks

A collection of LightGBM callbacks. (DART early stopping, tqdm progress bar)

bleedml

Bleeding Edge machine learning algorithms

scikit-stan

A sklearn style interface to Stan regression models

inne

A scikit-learn-compatible module for Isolation-based anomaly detection using nearest-neighbor ensembles

datefeatures

Feature engineering sklearn transformer for dates

thermoencoder

thermometer encoding with sklearn interface

pipelinetweak

additional wrapper classes for the sklearn API

palladio

ParALleL frAmework for moDel selectIOn

ecf

Python package providing an anomaly (outlier and novelty) detector based on the empirical Christoffel function.

optiml

Optimizers for/and sklearn compatible Machine Learning models

pctl-scale

Scale a variable into an open interval (0,1) whereas values within a given lower and upper percentile maintain a linear relation, and outlier saturate towards the interval limits.