binary-classification

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

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

Binary and Categorical Focal loss implementation in Keras.

Credit risk analysis for credit card applicants

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 for best precision/recall.

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

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

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

1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

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

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

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

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

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

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

A set of deep learning models for FRB/RFI binary classification.

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

Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.