Python Decision-trees Resources

DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH The repository contains a list of the projects which I have worked on while reading the book Deep Lea

Python Decision Tree and Random Forest Decision Tree A Decision Tree is one of the popular and powerful machine learning algorithms that I have learne

30-Days-of-ML-Kaggle 🔥 About the Hands On Program 💻 Machine learning beginner → Kaggle competitor in 30 days. Non-coders welcome The program starts

EasyMCDM - Quick Installation methods Install with PyPI Once you have created your Python environment (Python 3.6+) you can simply type: pip3 install

Towards Return Parity in Markov Decision Processes Code for the AISTATS 2022 pap

CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors   In order to facilitate the res

OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment.

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

Minimal implementation of Decision Transformer: Reinforcement Learning via Sequence Modeling in PyTorch for mujoco control tasks in OpenAI gym

Learning Efficient Online 3D Bin Packing on Packing Configuration Trees This repository is being continuously updated, please stay tuned！ Any code con

Python red-black trees A Python implementation of red-black trees. This code was originally copied from programiz.com, but I have made a few tweaks to

Hatchet Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data. It is intended for analyzing

TorchGRL TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffi

This open source Python project allow you to create JSON data trees using Minmup.com. I try to develop this project all the time. But feel free to use :).

DecisionTransformer_StepbyStep Intro Decision Transformer: A brand new Offline RL Pattern. 这是关于NeurIPS 2021 热门论文Decision Transformer的复现。 👍 原文地址: Deci

In this project it was used some different Machine Learning models to identify the gender of a voice (Female or Male) based on some specific speech and voice attributes.

Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision

Decision Weights in Prospect Theory It's clear that humans are irrational, but how irrational are they? After some research into behavourial economics

Decision-Threshold-ML Tutorial for improve skills: 'Decision Threshold In Machine Learning' (from GeeksforGeeks) by Marcus Mariano For more informatio

MADT: Offline Pre-trained Multi-Agent Decision Transformer A link to our paper can be found on Arxiv. Overview Official codebase for Offline Pre-train

TART This project is a PyTorch implementation for Transition Matrix Representati

DI-smartcross DI-smartcross - Decision Intelligence Platform for Traffic Crossin

Mixed neural likelihood estimation for models of decision-making Mixed neural likelihood estimation (MNLE) enables Bayesian parameter inference for mo

Welcome to Carbon Insight Carbon Insight is a platform aiming to display the carbon neutralization roadmap for researchers, decision-makers, and other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver, the wheel size, gear shifting sequence by modeling drivetrain constraints to achieve maximum laps in a race with a 2-hour time window.

Python Machine Learning Jupyter Notebooks (ML website) Dr. Tirthajyoti Sarkar, Fremont, California (Please feel free to connect on LinkedIn here) Also

Tree Substitution Cipher Generation Algorithm: Generate random tree. Tree nodes

DAT8 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15). Instructor: Kevin Markham (

msprime msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consis

Dir Tree Artist 🎨 🌲 Intro Easily view folder structure, with parameters to sieve out what you want. Choose to exclude files from being viewed (.git,

Overview This repository implements some common path planning algorithms used in robotics, including Search-based algorithms and Sampling-based algori

Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers and helping them make a wise buying decision.

VAST VAST - Visualise Abstract Syntax Trees for Python. VAST generates ASTs for a given Python script and builds visualisations of them. Install Insta

Total number of Spanning Trees in a Graph This is a python script just written f

django-treenode Probably the best abstract model / admin for your tree based stuff. Features Fast - get ancestors, children, descendants, parent, root

Final Project for the CS238: Decision Making Under Uncertainty course at Stanford University in Autumn '21. We optimized wind turbine placement in a wind farm, subject to wake effects, using Q-learning.

Introduction This is a simple program is written in python and turtle library. The objective of this program is to wish merry Christmas programmatical

Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.

TACT: Taxonomic Addition for Complete Trees TACT is a Python app for stochastic polytomy resolution. It uses birth-death-sampling estimators across an

Decision_Tree_Regression I implemented the decision tree regression algorithm on Python. Unlike regular linear regression, this algorithm is used when

Adjusting Decision Boundary for Class Imbalanced Learning This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting De

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version) This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural

Patch-wise Adversarial Removal Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal

Configurations Change HOME_PATH in CONFIG.py as the current path Data Prepare CENSINCOME Download data Put census-income.data and census-income.test i

Inspiration We shared university first year game coursework.[to gauge previous experience and start brainstorming] Adapted a particular nuclear fallou

LLVM-based compiler for LightGBM gradient-boosted trees. Speeds up prediction by ≥10x.

datastructures datastructures such as linked list, trees, graphs etc Made a public repository for coding enthusiasts. Those who want to collaborate on

dtreeviz : Decision Tree Visualization Description A python library for decision tree visualization and model interpretation. Currently supports sciki

Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

BartPy Introduction BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1]. Reasons to use BART

GROOT: Growing Robust Trees Growing Robust Trees (GROOT) is an algorithm that fits binary classification decision trees such that they are robust agai

Generalized Decision Transformer for Offline Hindsight Information Matching [arxiv] If you use this codebase for your research, please cite the paper:

AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto

music-recommender-rest-api A music recommendation REST API which makes a machine learning algorithm work with the Django REST Framework How it works T

highway-env A collection of environments for autonomous driving and tactical decision-making tasks An episode of one of the environments available in

CID3 This is a full-featured Decision Trees and Random Forests learner. It can save trees or forests to disk for later use. It is possible to query tr

Music Trees Supplementary code for the experiments described in the 2021 ISMIR submission: Leveraging Hierarchical Structures for Few Shot Musical Ins

slim-python is a package to learn customized scoring systems for decision-making problems. These are simple decision aids that let users make yes-no p

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Map single-cell transcriptomes to copy number evolutionary trees. Check out the tutorial for more information. Installation $ pip install scatrex SCA

dbv Decision Border Visualizer for Classification Algorithms Project description A python package for Machine Learning Engineers who want to visualize

Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leor

Active-LATHE: An Active Learning Algorithm for Boosting the Error exponent for Learning Homogeneous Ising Trees This project contains the codes of pap

Conditioning Sparse Variational Gaussian Processes for Online Decision-making This repository contains a PyTorch and GPyTorch implementation of the pa

DecisionTree A simple python implementation of Decision Tree, using Gini index. Usage: import DecisionTree node = DecisionTree.trainDecisionTree(lab

Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th

Stochastic Gradient Trees - Python Stochastic Gradient Trees1 by Henry Gouk, Bernhard Pfahringer, and Eibe Frank implementation in Python. Based on th

napkinXC napkinXC is an extremely simple and fast library for extreme multi-class and multi-label classification, that focus of implementing various m

Decision Tree Writer This package allows you to train a binary classification decision tree on a list of labeled dictionaries or class instances, and

Universal Reddit Scraper - A comprehensive Reddit scraping command-line tool written in Python.

Data Efficient Decision Making

Project Azua 0. Overview Many modern AI algorithms are known to be data-hungry, whereas human decision-making is much more efficient. The human can re

Soft-Decision-Tree Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published

Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah

pip-audit pip-audit is a prototype tool for scanning Python environments for packages with known vulnerabilities. It uses the Python Packaging Advisor

DecisionTree 决策树分类与回归模型，以及可视化 DecisionTree ID3 C4.5 CART 分类 回归 决策树绘制 分类树 回归树 调参 剪枝 ID3 ID3决策树是最朴素的决策树分类器： 无剪枝 只支持离散属性 采用信息增益准则 在data.py中，我们记录了一个小的西瓜数据

Decentralized Reinforcement Learning This is the code complementing the paper Decentralized Reinforcment Learning: Global Decision-Making via Local Ec

Bonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives.

A python library to build Model Trees with Linear Models at the leaves.

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Intruder-Detection Intruder detection systems are common place now, and readily available in industry, but how do they work? They must detect people a

Due to a normalization bug the expert trajectories have lower performance than the rl_baseline_zoo reported experts. Please see the following link in

Decision Transformer Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor M

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Neural Oblivious Decision Ensembles A supplementary code for anonymous ICLR 2020 submission. What does it do? It learns deep ensembles of oblivious di

Algorithmic Trading This machine learning algorithm was built using Python 3 and scikit-learn with a Decision Tree Classifier. The program gathers sto

Markov Decision Process A Markov decision process (MDP), by definition, is a sequential decision problem for a fully observable, stochastic environmen

The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. I

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

TreeInterpreter Package for interpreting scikit-learn's decision tree and random forest predictions. Allows decomposing each prediction into bias and

Contrastive Explanation (Foil Trees) Contrastive and counterfactual explanations for machine learning (ML) Marcel Robeer (2018-2020), TNO/Utrecht Univ

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

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r