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

Machine Unlearning for Random Forests

Exploring QSAR Models for Activity-Cliff Prediction

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

Analytics labs notebooks for Statistics and Business School students

NeuroData's package for exploring and using progressive learning algorithms

Conceptual & empirical comparisons between decision forests & deep networks

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

Gini feature importance for RankLib random forests:

Classifying Criminal Offenses: Classification Application in Python Using scikit-learn

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.

Material associated to the publication project on local trees methods for classification

A New, Interactive Approach to Learning Python

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

RFA package for implementing random forest adjustment.

Comparison of tree ensemble machine learning methods in predicting revenue outcome for an e-commerce site

Uso de Machine Learning para Predição de Volumes em Mananciais

R code for "Predicting predator-prey interactions in terrestrial endotherms using random forest"

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.