Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 13, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
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 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.
Boosted Hybrids of ensemble gradient algorithm for the long-term time series forecasting (LTSF)
A game theoretic approach to explain the output of any machine learning model.
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
A self-generalizing, hyperparameter-free gradient boosting machine
Predicting whether Lisbon MBA Toastmasters Club members have remained in the club or left it.
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.
Feature Engineering, Regression, Classification, Model Explanation. My 2 biggest projects exploring the link between economic indicators and U.S. presidential election results.
This repository contains implementations of regression models on the Starbucks stock market. The goal is to provide a comprehensive understanding of the performance of these models. Also, implement metrics without relying on external machine learning libraries. ☕️📈
🔍✨ A machine learning project that predicts income based on various demographic factors using Random Forest and Gradient Boosting algorithms. Includes data preprocessing, hyperparameter tuning, and model evaluation with detailed performance metrics. 📊🤖
This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.
Applied Data Science Project
A collection of boosting algorithms written in Rust 🦀
Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
Master the essentials of data science and machine learning by building a used car price prediction model from scratch, turning raw data into accurate pricing insights
This project implements a stock price prediction model using various technical indicators and an ensemble of machine learning algorithms. The model predicts the direction of price movements and provides price predictions with uncertainty bounds for the next 8 hours.
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