A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Phylogenetic classification of a species by way of machine learning modeling; feature selection preprocessing and construction of decision trees.

Tools created for machine learning classification model evaluation

This notebook explores fraud detection using various machine learning techniques.

Data Mining and Machine Learning Group Coursework

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.

A collection of 8 Applied Data Science projects.

Jointly-trained tree kernels for Gaussian processes

This project is a capstone part of the Google Advanced Data Analytics Professional Certificate on Coursera. This project involves data preparation and cleaning, exploratory data analysis (EDA), feature engineering, and model building and evaluation. Machine learning techniques are Logistic Regression, Decision Tree, Random Forest and XGBoost.

University assignments and sandbox projects connected to machine learning study.

solving problems with data analysis, hypotheses and the most used statistical tests in ecology

🍊 📊 💡 Orange: Interactive data analysis

Source code for augmenting relational datasets through join paths

An analysis on Iris dataset of Scikit learn

Investment Analysis and Asset Mgmt, Time Series Analysis & Forecasting, Machine Learning in Finance & Causal Inference Methods

Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference

An R package to create and visualise fast-and-frugal decision trees (FFTs)

Scripts from laboratories of course "Selected Topics of Machine Learning" from my 1st semester of Masters