random-forests

PacktWorkshops/The-Python-Workshop

A New, Interactive Approach to Learning Python

FarrellDay/miceRanger

miceRanger: Fast Imputation with Random Forests in R

jjfiv/fastrank

My most frequently used learning-to-rank algorithms ported to rust for efficiency. Try it: "pip install fastrank".

forestry-labs/Rforestry

neurodata/ProgLearn

NeuroData's package for exploring and using progressive learning algorithms

CBravoR/AdvancedAnalyticsLabs

Analytics labs notebooks for Statistics and Business School students

csinva/disentangled-attribution-curves

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

MarkusFerdinandDablander/QSAR-activity-cliff-experiments

Exploring QSAR Models for Activity-Cliff Prediction

bcgov/wetlandmapR

Scripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS

jjbrophy47/dare_rf

Machine Unlearning for Random Forests

neurodata/df-dn-paper

Conceptual & empirical comparisons between decision forests & deep networks

darth-c0d3r/deep-neural-decision-forests

A model combining Deep Neural Networks and (Stochastic) Random Forests.

tweichle/Predicting-Baseball-Statistics

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

jseluis/artificial_intelligence_for_trading

Artificial Intelligence for Trading

UnixJunkie/orf

OCaml Random Forests

TrainingByPackt/The-Python-Workshop

Become a proficient, productive and powerful programmer with Python

jgeofil/mycorrhiza-algorithm

Combining phylogenetic networks and Random Forests for prediction of ancestry from multilocus genotype data

Armin-Abdollahi/Machine-Learning

BioGenies/CancerGram

Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI.

eacheson/machine-learning-gazetteer-matching

Cross-gazetteer record linking of natural features in Switzerland using machine learning (random forests) and handcrafted rules.

lyraxvincent/Data-Science-Machine-Learning-Portfolio

Portfolio Projects through my Data Science and Machine Learning Course program.

Shahrukh2016/Retail_Sales_Prediction

Revolutionize sales forecasting for Rossmann stores with our high-accuracy XGBoost model, leveraging data analysis, feature engineering, and machine learning to predict sales up to six weeks in advance.

jjfiv/poetry-identification

Poetry Identification Code from my dissertation runs on zip files containing DJVUXML from the Internet Archive.

mlpapers/ensemble-learning

Awesome papers on Ensemble Learning

rlleshi/MachineLearning

Some fundamental machine learning and data analysis techniques are revisited here through practical projects

saimj7/Machine-Learning-Algorithms

ML algorithms and applications from famous papers; simple theory; solid code.

sorooshsorkhani/RankLib-Gini

Gini feature importance for RankLib random forests:

JalalAl-Tamimi/Intro-Random-Forests

This repo contains material for a workshop on Random Forests in phonetics/phonology research

milesdwilliams15/RFA

RFA package for implementing random forest adjustment.

msarnacki/online-news-popularity-classification

This was a binary classification task in which I had to determine if and article got at least 1400 shares. I wanted to use few different machine learning algorithms to compare their accuracy on that data. I chose to use: Decision Tree, Random Forests and Multi Layer Perceptron.

chloestipinovich/Grass-Pollen-Forecasting

This is the repository used to make the project titled 'Grass Pollen in Cape Town: A Comparison of Generalised Additive Models and Random Forests' by Sky Cope and Chloë Stipinovich.

jesussantana/IBM-Machine-Learning-with-Python

This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language

Lavanyask03/income

Binary Classification of incomes as <50k or >50k using decision trees and random forests.

MohammadvHossein/ML-GYM

The ML-GYM repository showcases machine learning projects using **scikit-learn**, covering classification, regression, and clustering. It offers educational resources for beginners and practical examples for experienced users, complete with detailed instructions.

TrentBrunson/Machine_Learning

Supervised Machine Learning using SciKit and other tools to do PCA, SVM, random forests, etc. for facial recognition and predictive decision making.