binary-classification

A multi-platform GUI for bit-based analysis, processing, and visualization

Fast and customizable framework for automatic ML model creation (AutoML)

Binary and Categorical Focal loss implementation in Keras.

Code for Java Deep Learning Cookbook

autosklearn-zeroconf is a fully automated binary classifier. It is based on the AutoML challenge winner auto-sklearn. Give it a dataset with known outcomes (labels) and it returns a list of predicted outcomes for your new data. It even estimates the precision for you! The engine is tuning massively parallel ensemble of machine learning pipelines…

Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0

Credit risk analysis for credit card applicants

2017-CCF-BDCI-企业经营退出风险预测：9th/569 (Top 1.58%)

Validation of binary classifiers and data used to develop them

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

2018 - Kaggle - TalkingData AdTracking Fraud Detection Challenge: Silver medal (银牌)

Iguanas is a fast, flexible and modular Python package for generating a Rules-Based System (RBS) for binary classification use cases.

A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.

The binclass-tools package contains a set of Python wrappers and interactive plots that facilitate the analysis of binary classification problems.

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

2018-腾讯广告算法大赛-相似人群拓展(初赛)：10th/1563 (Top 0.64%)

Detecting Autism Spectrum Disorder in Children With Computer Vision - Adapting facial recognition models to detect Autism Spectrum Disorder

Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data