Search Results for "decision tree in matlab"
Sort By:
Showing 62 open source projects for "decision tree in matlab"
View related business solutions-
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
-
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
-
1
dtreeviz
Python library for decision tree visualization & model interpretation
A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. -
2
MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
3
LightGBM
Gradient boosting framework based on decision tree algorithms
LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle large-scale data. ... -
4
FLEXible
Federated Learning (FL) experiment simulation in Python
FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees. -
Catch Bugs Before Your Customers DoMove from alert to fix before users notice. AppSignal monitors errors, performance bottlenecks, host health, and uptime—all from one dashboard. Instant notifications on deployments, anomaly triggers for memory spikes or error surges, and seamless log management. Works out of the box with Rails, Django, Express, Phoenix, Next.js, and dozens more. Starts at $23/month with no hidden fees.
-
5
StringTemplate-for-Octave
Interface for GNU Octave/MATLAB with StringTemplate V4 template engine
This project provides a generic interface for GNU Octave with Terence Parr's popular StringTemplate V4 template engine; MATLAB is supported, too. The concept is simple. An Octave script wraps your data in suitable Java collections, so that deeply nested data structures can be passed to the template engine just like that - and without the need for a compiled Java interface class, as it would when directly building on Octave's generic Java interface to access StringTemplate. -
6
GeoDMA
Geographic feature extraction and data mining
GeoDMA is a plugin for TerraView software, used for geographical data mining. With a single image, the user can perform segmentation, attributes extraction, normalization and classification. -
7
DecisionTree.jl
Julia implementation of Decision Tree (CART) Random Forest algorithm
Julia implementation of Decision Tree (CART) and Random Forest algorithms. -
8
Repo of Tree of Thoughts (ToT)
Implementation of "Tree of Thoughts
Language models are increasingly being deployed for general problem-solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem-solving. ... -
9
NaveGo
NaveGo: an open source MATLAB/GNU Octave toolbox for processing integr
NaveGo is an open source MATLAB/GNU Octave toolbox designed for processing integrated navigation systems, simulating inertial sensors and GNSS receivers, and profiling inertial sensors using methods like Allan variance—providing a community-driven simulation framework for navigation system design and analysis. I am reaching out to share an important update regarding the NaveGo project. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
Awesome Decision Tree Papers
A collection of research papers on decision, classification, etc.
A collection of research papers on decision, classification and regression trees with implementations. -
11
ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
...Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. If you want more control, you can provide a config file. -
12
treeminder
a free mind mapping tool, to especially assist decision making
I've been trying to develop an idea of a project, but the wide range of possibilities with all their outcomes, cons and pros and whatnot got my mind overloaded. There was no way I could keep or process that much information with my bare mind. So, I've checked the mind mapping tools available in the market, both free and commercial ones, yet they were either clunky or overkilling for my needs. Thus, I've made my own thing, and decided to share it with everyone who might find it useful for... -
13
Toxtree: Toxic Hazard Estimation
Toxicity prediction for chemical compounds
...The latest version includes the following toxicity prediction modules: -Cramer rules (oral toxicity) -Toxicity mode of action via Verhaar scheme -Skin irritation and Eye irritation prediction -Benigni / Bossa rulebase for mutagenicity and carcinogenicity prediction -START biodegradation and persistence prediction -Skin sensitisation reactivity domain -Kroes TTC Decision tree -SMARTCyp - Cytochrome P450-Mediated Drug Metabolism and metabolites prediction -Structure Alerts for the in vivo micronucleus assay in rodents (ISSMIC) -Structural Alerts for Functional Group Identification (ISSFUNC) -Structural alerts associated with covalent protein binding and DNA binding. - Ames mutagenicity Toxtree provides a plugin framework to incorporate different approaches to the estimation. ... -
14
Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying solely on black-box frameworks. ... -
15
ASCII Art
Real-Time ASCII Art Rendering Library
A header-only, C/C++ library that lets you transform an input image or video frame into printable ASCII characters in real-time using a single decision tree. ASCII Art is a single header, C/C++ library developed by PixLab and based on the work of Nenad Markus that let you transform an input image or video frame into printable ASCII characters at real-time using a single decision tree. Real-time performance is achieved by using pixel intensity comparison inside the internal nodes of the tree. -
16
Varied Tree Weka Package
Random Tree-based classifier featuring multiple node tests.
Weka package extension with custom decision tree classifier (based on Random Tree) generating three different tests in its nodes: traditional value test (e.g. x1 < 3.4), relational test (e.g. x1 < x2) and weighted relational test (e.g. x1 < 0.78 * x2). Decision on node level is made based on weighted vote made by each test. -
17
Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
18
CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
19
CodelPlant
Codelplant stat. multi-regression M-learning for leaves segmentation
CodelPlant is based on the creation of a statistical multi-regression decision tree from data obtained by pixel RGB-HSB machine learning analysis. The model is training with human observation, so the human factor is being entered the process. -
20
Simple Value Tree is an Excel Add-in for Multiple-Criteria Decision Analysis (MCDA). This Add-in was created by Huong Lien Le and Gilberto Montibeller in 2017, and further developed by Hangchen Zhu in 2018.
-
21
EEG Seizure Prediction
Seizure prediction from EEG data using machine learning
The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy. -
22
MYRA
A collection of ACO algorithms for the data mining classification task
MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task. It includes popular rule induction and decision tree induction algorithms. The algorithms are ready to be used from the command line or can be easily called from your own Java code. They are build using a modular architecture, so they can be easily extended to incorporate different procedures and/or use different parameter values. This project is now hosted at: https://github.com/febo/myra -
23
faif
C++ header only library with AI and bioinformatics algorithms
C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others. -
24
VeriSiMPL
Verification via biSimulations of Max-Plus-Linear models
...If you are more familiar with MATLAB language, we suggest you to try VeriSiMPL version 1.4 which is fully based on MATLAB. -
25
This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. ...
