88 Open source projects that are alternatives of or similar to GROOT

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

General Assembly's 2015 Data Science course in Washington, DC

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

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

📈 Click-Through Rate Prediction using Logistic Regression and Tree Algorithms

Hyperon - Motor Insurance Demo App. This is a sample application to demonstrate capabilities of Hyperon.io library (Java Business Rules Engine (BRE)/Java Pricing Engine). The application demonstrates responsive quotations for Car/Motor Insurance based on decision tables and Rhino functions (for math calculations). It shows different possible bus…

Tajweed annotation for the Qur'an

A python implementation of C4.5 algorithm by R. Quinlan

Seminar work "Decision Trees - An Introduction" with presentation, seminar paper, and Python implementation

A tool for visualizing the structure and performance of Random Forests 🌳

Implemention of Fast Gradient Sign Method for generating adversarial examples in Keras

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

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.

An Interactive Approach to Understanding Supervised Learning Algorithms

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

Machine Learning models from scratch with a better visualisation

Text Classification Algorithms: A Survey

A lightweight library for designing and executing workflows in .NET Core

Software for creating and analyzing decision trees.

Interactive GUI (developed in Python) for calibration and conditional verification of numerical weather prediction model outputs.

🌳 Stacked Gradient Boosting Machines

Heatmap-integrated Decision Tree Visualizations

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Code for the unrestricted adversarial examples paper (NeurIPS 2018)

Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)

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.

Code for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

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.

🍊 📊 💡 Orange: Interactive data analysis

A Closer Look at Accuracy vs. Robustness

🔥🌟《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!

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

Code to reproduce the attacks and defenses for the entries "JeromeR" in the NIPS 2018 Adversarial Vision Challenge

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

Real time face landmarking using decision trees and NN autoencoders

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Rubi for Mathematica

Go Scoring API for PMML

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)

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

This is the implementation of Sparse Projection Oblique Randomer Forest

Randomized Smoothing of All Shapes and Sizes (ICML 2020).

R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)

Machine Learning University: Decision Trees and Ensemble Methods

For extensive instructor led learning

A complete one stop solution for all the problems of Rural area people. 👩🏻‍🌾

An Interactive Approach to Understanding Deep Learning with Keras

adversarial examples, adversarial malware examples, adversarial malware detection, adversarial deep ensemble, Android malware variants

java math expression parser is faster than JEP

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

Final project of IBM's course https://www.coursera.org/learn/machine-learning-with-python on coursera

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…

Implementations of machine learning algorithm by Python 3

Misc Statistics and Machine Learning codes in R