Julia implementation of Decision Tree (CART) and Random Forest algorithms

A New, Interactive Approach to Learning Python

miceRanger: Fast Imputation with Random Forests in R

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

NeuroData's package for exploring and using progressive learning algorithms

Analytics labs notebooks for Statistics and Business School students

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

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

Machine Unlearning for Random Forests

Conceptual & empirical comparisons between decision forests & deep networks

Exploring QSAR Models for Activity-Cliff Prediction

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

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

Artificial Intelligence for Trading

OCaml Random Forests

Become a proficient, productive and powerful programmer with Python

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

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.

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.