#Decision-trees
Showing 56 of 56 repositories tagged #decision-trees, ranked by stars
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.
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
๐ :bar_chart: :bulb: Orange: Interactive data analysis
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
A python library for decision tree visualization and model interpretation.
For extensive instructor led learning
Text Classification Algorithms: A Survey
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.
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Impress your boss with interactive Decision Tree visualization
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
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by โฅ10x.
Machine Learning Lectures at the European Space Agency (ESA) in 2018
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
Simple machine learning library / ็ฐกๅฎๆ็จ็ๆฉๅจๅญธ็ฟๅฅไปถ
Estudo e implementaรงรฃo dos principais algoritmos de Machine Learning em Jupyter Notebooks.
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.
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
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)
Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
Beta Machine Learning Toolkit
Scikit-learn compatible decision trees beyond those offered in scikit-learn
Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.
Real time face landmarking using decision trees and NN autoencoders
Data Science, Machine Learning, Deep Learning, NLP, Python & Library's cheat Sheet - Interview Questions & Notes
Heatmap-integrated Decision Tree Visualizations
Small project to learn neuronal network with classifying some basic colors based on Decision Tree Classifier and webcam input
Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.
c++ implementation of decision tree algorithm
Evolutionary Pac-Man bots using Grammatical Evolution and Multi-objective Optimization. Cool GUI included (Bachelor Thesis)
This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"
๐ฒ Decision Tree Visualization for Apache Spark
Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG
๐ 30 Days of Data Science is a daily challenge to guide you through Data Science essentials. From basics to advanced, this repo offers clear examples, practical exercises, and resources to help you master Data Science, one day at a time. Whether you're new or refining your skills, this challenge has something for you. Join the journey now! ๐
Corpus and a baseline neural network system for Named Entity Recognition in Hindi-English Code-Mixed social media text.
ๆญฆๆฑๅคงๅญฆๆฐๆฎ็งๅญฆๅฏผ่ฎบ
A C++ project in which you can play chess against an AI that uses alpha-beta pruning to predict the optimal move.
Misc Statistics and Machine Learning codes in R
Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.
Alzheimer Disease Detection Model for Real Time Hospital Usage
Signal Processing Toolkit, including ML models with visualization
Predicting air pollution level in a specific city
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
privacy-first context graph engine for AI agents and human teams.
This repository should help people that would like to code in R and work with the National Health and Nutrition Examination Survey (NHANES). Some topics corved are SQL , logistic regression.... etc
An Interactive Approach to UnderstandingโฏSupervised LearningโฏAlgorithms
EDA and Machine Learning Models in R and Python (Regression, Classification, Clustering, SVM, Decision Tree, Random Forest, Time-Series Analysis, Recommender System, XGBoost)
Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.
Lung Cancer Prediction using Machine Learning Algorithms
Experiments with experimental rule-based models to go along with imodels.
iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python