binary-classification

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

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

Credit risk analysis for credit card applicants

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

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

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

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

Multi-modal AI-generated content detection: image, video, and audio. Benchmarks, training code (DINOv2, DINOv3, ReStraV, BreathNet), and evaluation pipeline for real vs. synthetic classification with calibration-aware metrics.

Implementation of Focal Loss (Lin et al., 2017, Facebook AI Research) for handling class imbalance by focusing learning on hard, misclassified examples.

🧹 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.