🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库

Snapshot Ensemble in Keras

[ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

Open solution to the Toxic Comment Classification Challenge

This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

Semi-supervised anomaly detection method

Open solution to the Santander Value Prediction Challenge 🐠

AICUP 2024 Cross-camera Multiple-object tracking

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

Bayesian Reward Shaping Framework for Deep Reinforcement Learning

Fake News Detection - Feature Extraction using Vectorization such as Count Vectorizer, TFIDF Vectorizer, Hash Vectorizer,. Then used an Ensemble model to classify whether the news is fake or not.

Feature selection method based on repeated elastic net.

The code of Team Rhinobird for Mining the Web of HTML-embedded Product Data Task One at ISWC2020

A scikit-learn-compatible module for Isolation-based anomaly detection using nearest-neighbor ensembles

Code to implement the Runner-up Solution to the CVPR'23 SoccerNet Challenge (Ball Action Spotting Task)

Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution

This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.

Multiple Model Ensembling

Public repository for a paper in UAI 2019 describing adaptive epsilon-greedy exploration using Bayesian ensembles for deep reinforcement learning.