Popular GitHub Repositories for Decision Tree Regressor

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

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.

A CLI tool to convert your codebase into a single LLM prompt with source tree, prompt templating, and token counting.

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Official PyTorch implementation of PTS/PSRN: Fast and efficient symbolic expression discovery through parallelized tree search. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework. Website: https://swarms.ai

A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A python library for decision tree visualization and model interpretation.

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.

behaviac is a framework of the game AI development, and it also can be used as a rapid game prototype design tool. behaviac supports the behavior tree, finite state machine and hierarchical task network(BT, FSM, HTN)

A minimalist environment for decision-making in autonomous driving