decision-trees
There are 3155 repositories under decision-trees topic.
microsoft/LightGBM
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.
catboost/catboost
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.
biolab/orange3
🍊 :bar_chart: :bulb: Orange: Interactive data analysis
susanli2016/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
tirthajyoti/Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
parrt/dtreeviz
A python library for decision tree visualization and model interpretation.
greyhatguy007/Machine-Learning-Specialization-Coursera
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
kk7nc/Text_Classification
Text Classification Algorithms: A Survey
edyoda/data-science-complete-tutorial
For extensive instructor led learning
justmarkham/DAT8
General Assembly's 2015 Data Science course in Washington, DC
nivu/ai_all_resources
A curated list of Best Artificial Intelligence Resources
tensorflow/decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
alvinwan/neural-backed-decision-trees
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
serengil/chefboost
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
google/yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
dmitryikh/leaves
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
mdabros/SharpLearning
Machine learning for C# .Net
RGF-team/rgf
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.
Skylark0924/Machine-Learning-is-ALL-You-Need
🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
jmartinezheras/2018-MachineLearning-Lectures-ESA
Machine Learning Lectures at the European Space Agency (ESA) in 2018
cerlymarco/linear-tree
A python library to build Model Trees with Linear Models at the leaves.
jayshah19949596/Machine-Learning-Models
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
siboehm/lleaves
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
fukuball/fuku-ml
Simple machine learning library / 簡單易用的機器學習套件
CynthiaKoopman/Network-Intrusion-Detection
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
aws-samples/aws-machine-learning-university-dte
Machine Learning University: Decision Trees and Ensemble Methods
RuleBasedIntegration/Rubi
Rubi for Mathematica
quocviethere/Coursera-Machine-Learning-Specialization
2022 Coursera Machine Learning Specialization Optional Labs and Programming Assignments
arnaldog12/Machine_Learning
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
rolkra/explore
R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)
dlab-berkeley/Machine-Learning-in-R
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
milaan9/Machine_Learning_Algorithms_from_Scratch
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
rojaAchary/30-Days-of-ML-Kaggle
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.
MBKraus/Predicting_real_estate_prices_using_scikit-learn
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
psaris/funq
Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
Mayurji/MLWithPytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch