Test symmetries with sklearn decision tree models

Last update: Jul 19, 2022

Overview

Test symmetries with sklearn decision tree models

Setup

Begin from an environment with a recent version of python 3.

source setup.sh

Leave the environment with deactivate. Clean up fully by removing env/.

Run examples

make

Independence is worth duplication (an excuse for this code structure)

Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class

482 Nov 19, 2022

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

6.9k Jan 5, 2023

Data Efficient Decision Making

197 Jan 6, 2023

MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

61 Dec 19, 2022

Machine Learning Algorithms ( Desion Tree, XG Boost, Random Forest )

implementation of machine learning Algorithms such as decision tree and random forest and xgboost on darasets then compare results for each and implement ant colony and genetic algorithms on tsp map, play blackjack game and robot in grid world and evaluate reward for it

1 Jan 19, 2022

A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo

🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

1.4k Jan 6, 2023

[HELP REQUESTED] Generalized Additive Models in Python

pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized

747 Jan 5, 2023

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

129 Dec 24, 2022

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

519 Jan 3, 2023

Releases(zenodo-1)

zenodo-1(Jul 19, 2022)

First version to go on Zenodo 🤖 second major version ♊
Source code(tar.gz)
Source code(zip)

Owner

Rupert Tombs

happiest when bit twiddling

GitHub Repository

Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.

Simple but maybe too simple config management through python data classes. We use it for machine learning.

67 Nov 29, 2022

Combines Bayesian analyses from many datasets.

PosteriorStacker Combines Bayesian analyses from many datasets. Introduction Method Tutorial Output plot and files Introduction Fitting a model to a d

19 Feb 13, 2022

This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev

MLProject_01 This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev Context Dataset English question data set file F

1 Dec 18, 2021

Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

419 Jan 01, 2023

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Intel(R) Extension for Scikit-learn* Installation | Documentation | Examples | Support | FAQ With Intel(R) Extension for Scikit-learn you can accelera

858 Dec 25, 2022

Fourier-Bayesian estimation of stochastic volatility models

fourier-bayesian-sv-estimation Fourier-Bayesian estimation of stochastic volatility models Code used to run the numerical examples of "Bayesian Approa

15 Jun 20, 2022

This is a Machine Learning model which predicts the presence of Diabetes in Patients

Diabetes Disease Prediction This is a machine Learning mode which tries to determine if a person has a diabetes or not. Data The dataset is in comma s

4 Mar 16, 2022

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

1 Nov 22, 2021

使用数学和计算机知识投机倒把

偷鸡不成项目集锦坦率地讲，涉及金融市场的好策略如果公开，必然导致使用的人多，最后策略变差。所以这个仓库只收集我目前失败了的案例。加密货币组合套利中国体育彩票预测我赚不上钱的项目，也许可以帮助更有能力的人去赚钱。

28 Dec 29, 2022

🤖 ⚡ scikit-learn tips

🤖 ⚡ scikit-learn tips New tips are posted on LinkedIn, Twitter, and Facebook. 👉 Sign up to receive 2 video tips by email every week! 👈 List of all

1.6k Jan 03, 2023

Climin is a Python package for optimization, heavily biased to machine learning scenarios

climin climin is a Python package for optimization, heavily biased to machine learning scenarios distributed under the BSD 3-clause license. It works

177 Sep 02, 2022

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

519 Jan 03, 2023

Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm.

Naive-Bayes Spam Classificator Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm. Main goal is to code a

1 Jun 27, 2022

ETNA is an easy-to-use time series forecasting framework.

ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from

674 Jan 07, 2023

Machine Learning University: Accelerated Natural Language Processing Class

Machine Learning University: Accelerated Natural Language Processing Class This repository contains slides, notebooks and datasets for the Machine Lea

2k Jan 01, 2023

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

3k Jan 08, 2023

Test symmetries with sklearn decision tree models

Related tags

Overview

Test symmetries with sklearn decision tree models

Setup

Run examples

Independence is worth duplication (an excuse for this code structure)

You might also like...

Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Data Efficient Decision Making

MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

Machine Learning Algorithms ( Desion Tree, XG Boost, Random Forest )

A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo

[HELP REQUESTED] Generalized Additive Models in Python

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Releases(zenodo-1)

zenodo-1(Jul 19, 2022)

Owner

Rupert Tombs

Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.

Combines Bayesian analyses from many datasets.

This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev

Automatically create Faiss knn indices with the most optimal similarity search parameters.

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Fourier-Bayesian estimation of stochastic volatility models

This is a Machine Learning model which predicts the presence of Diabetes in Patients

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

使用数学和计算机知识投机倒把

🤖 ⚡ scikit-learn tips

Climin is a Python package for optimization, heavily biased to machine learning scenarios

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm.

ETNA is an easy-to-use time series forecasting framework.

Machine Learning University: Accelerated Natural Language Processing Class

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

End to End toy example of MLOps

Tribuo - A Java machine learning library

pandas, scikit-learn, xgboost and seaborn integration

Create large-scale ML-driven multiscale simulation ensembles to study the interactions