GitHub topics: boosted-trees
crillab/pyxai
PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).
Language: Python - Size: 74.3 MB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 33 - Forks: 3
Evovest/EvoTrees.jl
Boosted trees in Julia
Language: Julia - Size: 44.8 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 182 - Forks: 22
PaoloGiordani/HybridTreeBoosting.jl
Hytrid Tree Boosting
Language: Julia - Size: 21.4 MB - Last synced at: 6 days ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 0
max-andr/provably-robust-boosting
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
Language: Python - Size: 7.14 MB - Last synced at: 27 days ago - Pushed at: almost 5 years ago - Stars: 50 - Forks: 12
wepe/tgboost
Tiny Gradient Boosting Tree
Language: Java - Size: 21.8 MB - Last synced at: 14 days ago - Pushed at: almost 6 years ago - Stars: 321 - Forks: 103
RM503/CMI-Problematic_Internet_Usage
Predictive modeling of the Kaggle contest hosted by the 'Child Mind Institute'
Language: Jupyter Notebook - Size: 5.53 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0
Azure/fast_retraining 📦
Show how to perform fast retraining with LightGBM in different business cases
Language: Jupyter Notebook - Size: 2.66 MB - Last synced at: 14 days ago - Pushed at: almost 6 years ago - Stars: 54 - Forks: 15
wkcn/mlkit
implement machine learning algorithm from scratch
Language: C++ - Size: 119 KB - Last synced at: about 1 year ago - Pushed at: almost 5 years ago - Stars: 4 - Forks: 0
rahulrajan15/Alteryx_DA_Project
Using Alteryx, analyze customer data to forecast future issues, support business growth, address challenges, and provide tailored solutions.
Size: 4.76 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0
nicholasjclark/Mediterranean-Fishes-MRF
R code to reproduce analyses in "Rapid winter warming could disrupt coastal marine fish community structure" (Clark et al, Nature Climate Change, 2020)
Language: R - Size: 36.1 MB - Last synced at: about 1 year ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 2
suzanaiacob/Tree-Methods
Projects using tree methods (CART, Random Forests, Boosted Trees)
Size: 1.01 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 0
adamlilith/enmSdm 📦
Faster, better, smarter ecological niche modeling and species distribution modeling
Language: R - Size: 502 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 44 - Forks: 19
starryjay/PSTAT131Final
This is the repository for my R project on modeling historical weather data in Santa Barbara.
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maduprey/protein-structure-learning
Protein classification with deep learning and boosted trees using topological features
Language: Jupyter Notebook - Size: 75.5 MB - Last synced at: about 1 month ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0
nyk510/gradient-boosted-decision-tree
GBDT (Gradient Boosted Decision Tree: 勾配ブースティング) のpythonによる実装
Language: Python - Size: 2.54 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 49 - Forks: 6
jakobgerstenlauer/RottenOnions
We downloaded and processed ten years of historic log data from the Tor project. Then we used boosted regression trees and generalized linear models to predict malicious exit nodes.
Language: R - Size: 1.44 MB - Last synced at: over 1 year ago - Pushed at: almost 8 years ago - Stars: 3 - Forks: 1
stewarthkerr/CS760
CS760: Machine Learning
Language: Python - Size: 95.3 MB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 0 - Forks: 0
sakbarpu/TraditionalClassifiers
This repository implements the basic machine learning classifiers for the problem of Yelp reviews classification. We assume the problem to be a binary classification problem. The models implemented are Naive Bayes, Logistic Regression, Support Vector Machine (linear), Decision Trees, Bagged Decision Trees, Random Fforests, and Boosted Decision Trees.
Language: Python - Size: 2.01 MB - Last synced at: over 1 year ago - Pushed at: almost 7 years ago - Stars: 1 - Forks: 1
srinithish/Machine-Learning-Algorithms-From-Scratch
Neural Networks, Ada Boost, Random Forest, KNN, BoostedForest
Language: Python - Size: 5.91 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 1 - Forks: 0
eshan-kaul/DecisionTree.BostedTree.KernelPLS
Custom built Decision Tree + Boosted Trees + KernelPLS in python
Size: 9.99 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0
joe-arul/health_and_finance
Determining financial factors affecting the health of an individual
Language: HTML - Size: 2.5 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0
rndev2017/ExoBoost
Using radial velocity data to identify exoplanet companions
Language: Python - Size: 21.5 MB - Last synced at: 9 days ago - Pushed at: about 2 years ago - Stars: 3 - Forks: 0
msahamed/lending_loan_prediction
The purpose of this project is to process the dataset, analyze it, do some feature engineering and finally make a predictive loan model for an applicant.
Language: Jupyter Notebook - Size: 961 KB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 14 - Forks: 8
bckhm/Titanic-ML
Classifying the survival of passengers aboard the Titanic via the use of various Machine Learning algorithms.
Language: Jupyter Notebook - Size: 522 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0
nickkunz/treeforestboost
Classification Trees, Random Forest, Boosting | Columbia Business School
Language: R - Size: 10.9 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 2 - Forks: 0
EmmaSui/Classification--Predicting-Default-Risk
Classification prediction model
Language: Jupyter Notebook - Size: 223 KB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0
Yafang611/DeutscherAutomarkt
R based data analytics on German Cars Market
Language: R - Size: 461 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
chen0040/java-decision-forest
Package implements decision tree and isolation forest
Language: Java - Size: 165 KB - Last synced at: 19 days ago - Pushed at: almost 8 years ago - Stars: 12 - Forks: 6
mirugwe1/Accurate-occupancy-detection-of-an-office-room-from-light-temperature-humidity-and-CO2-measurement
This project aims at developing, validating, and testing several classification statistical models that could predict whether or not an office room is occupied using several data features, namely temperature (◦C), light (lx), humidity (%), CO2 (ppm), and a humidity ratio. The data is modeled using classification techniques i.e. Logistic regression, Classification tree, Bagging-Random forest, and Gradient boosted trees. These models were trained and then after evaluated against validation and test sets and using confusion matrices to obtain classification and misclassification rates. The logistic model was trained using glmnet R package, Tree package for classification tree model, randomForest for both Bagging and Random Forest Models, and gbm package for Gradient Boosted Model. The best accuracy was obtained from the Random Forest Model with a classification rate of 93.21% when it was evaluated against the test set. Light sensor is also the most significant variable in predicting whether the office room is occupied or not, this was observed in all the five models.
Size: 189 KB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 1
