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
Test symmetries with sklearn decision tree models
Overview
You might also like...
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
Data Efficient Decision Making
197 Jan 6, 2023
MBTR is a python package for multivariate boosted tree regressors trained in parameter space.
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](https://github.com/dswah/pyGAM/raw/master/imgs/pygam_tensor.png)
[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)
To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction
To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction. The challenge aims to adress the problems of medical imbalanced data classification.
1 Jan 31, 2022
Predicting job salaries from ads - a Kaggle competition
Predicting job salaries from ads - a Kaggle competition
57 Oct 23, 2020
Given the names and grades for each student in a class N of students, store them in a nested list and print the name(s) of any student(s) having the second lowest grade.
Hackerank-Nested-List Given the names and grades for each student in a class N of students, store them in a nested list and print the name(s) of any s
2 Dec 14, 2021
Practical Time-Series Analysis, published by Packt
Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj
325 Dec 23, 2022
AutoOED: Automated Optimal Experiment Design Platform
AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an
107 Jan 03, 2023
Pandas DataFrames and Series as Interactive Tables in Jupyter
Pandas DataFrames and Series as Interactive Tables in Jupyter Star Turn pandas DataFrames and Series into interactive datatables in both your notebook
364 Jan 04, 2023
Python module for performing linear regression for data with measurement errors and intrinsic scatter
Linear regression for data with measurement errors and intrinsic scatter (BCES) Python module for performing robust linear regression on (X,Y) data po
56 Sep 27, 2022
A toolkit for geo ML data processing and model evaluation (fork of solaris)
An open source ML toolkit for overhead imagery. This is a beta version of lunular which may continue to develop. Please report any bugs through issues
4 Nov 04, 2021
A benchmark of data-centric tasks from across the machine learning lifecycle.
A benchmark of data-centric tasks from across the machine learning lifecycle.
61 Dec 28, 2022
A naive Bayes model for cancer classification using a set of documents
Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The
1 Nov 24, 2021
Provide an input CSV and a target field to predict, generate a model + code to run it.
automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn
1.8k Jan 04, 2023
cleanlab is the data-centric ML ops package for machine learning with noisy labels.
cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and lear
51 Nov 28, 2022
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.
7.4k Jan 04, 2023
Python based GBDT implementation
Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A
20 Sep 21, 2022
Python ML pipeline that showcases mltrace functionality.
mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng
28 Nov 09, 2022
Lseng-iseng eksplor Machine Learning dengan menggunakan library Scikit-Learn
Kalo dengar istilah ML, biasanya rada ambigu. Soalnya punya beberapa kepanjangan, seperti Mobile Legend, Makan Lontong, Ma**ng L*v* dan lain-lain. Tapi pada repo ini membahas Machine Learning :)
1 Apr 06, 2022
Reproducibility and Replicability of Web Measurement Studies
Reproducibility and Replicability of Web Measurement Studies This repository holds additional material to the paper "Reproducibility and Replicability
6 Dec 31, 2022
Conducted ANOVA and Logistic regression analysis using matplot library to visualize the result.
Intro-to-Data-Science Conducted ANOVA and Logistic regression analysis. Project ANOVA The main aim of this project is to perform One-Way ANOVA analysi
1 Feb 06, 2022
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query
5 Aug 06, 2022
Implementation of deep learning models for time series in PyTorch.
List of Implementations: Currently, the reimplementation of the DeepAR paper(DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
275 Dec 28, 2022