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adaptiverandomforest

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

adaptivexgboostclassifier

Implementation of the Adaptive XGBoost classifier for evolving data streams

auto-stream

autogbt

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.

automl-streams

AutoML framework for implementing automated machine learning on data streams

causalml

Uplift modeling and causal inference with machine learning algorithms

cdcms

The implementation of the Concept Drift handling based on Clustering in the Model Space (CDCMS) algorithm, proposed in the paper “A Diversity Framework for Dealing with Multiple Types of Concept Drift Based on Clustering in the Model Space”, accepted by IEEE TNNLS 2020.

concept-drift-datasets-scikit-multiflow

concept drift datasets edited to work with scikit-multiflow directly

cs-arf

Compressed Sensing Adaptive Random Forest (CS-ARF)

cs-knn

Compressed Sensing k-Nearest Neighbors (CS-kNN)

data_streams

You will find (about) synthetic and real-world data streams in this repository.

diversitypool

The implementation of the Diversity Pool algorithm, proposed in the paper "Diversity-Based Pool of Models for Dealing with Recurring Concepts" and presented at IJCNN '18

ecmlpkdd2021

ECML-PKDD 2021

lifetimes

Lifetime value in Python

ml-talks

You may find my slides for different talks in this repo.

mlclass

My main Machine Learning class

mltalks

My slides and resources from various talks/workshops

onn

Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)

pypsi

A Python library for private set intersection

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

scene-recognition

Scene recognition by combining local and global image descriptors

scikit-multiflow

A machine learning framework for multi-output/multi-label and stream data.

secure-xgboost

Secure collaborative training and inference for XGBoost.

syntheticdata

Synthetic data streams to simulate diverse concept-drift scenarios. Data generation uses MOA 21.07.0

syntheticdata20

20 Synthetic data streams to simulate diverse concept-drift scenarios. Data generation uses MOA 21.07.0

syntheticdata6

6 Synthetic data streams to simulate diverse concept-drift scenarios. Data generation uses MOA 21.07.0

workshops