There are 20 repositories under decision-trees topic.
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 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.
Python code for common Machine Learning Algorithms
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Text Classification Algorithms: A Survey
For extensive instructor led learning
General Assembly's 2015 Data Science course in Washington, DC
A curated list of Best Artificial Intelligence Resources
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
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
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
🔥🌟《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!
Machine learning for C# .Net
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.
Machine Learning Lectures at the European Space Agency (ESA) in 2018
A python library to build Model Trees with Linear Models at the leaves.
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
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
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
Machine Learning University: Decision Trees and Ensemble Methods
Rubi for Mathematica
2022 Coursera Machine Learning Specialization Optional Labs and Programming Assignments
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
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.
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)
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch