Stars
A book for Learning the Foundations of LLMs
A Python Library for Creating Scorecards From Classification Models.
scorecardpipeline封装常用的风控策略分析和评分卡建模相关组件,支持pipeline式端到端评分卡建模、三方数据分析、规则集效果评估、特征有效性分析、excel报告输出、评分卡PMML导出、全流程超参数搜索等功能。核心功能:评分卡,策略分析,风控,规则挖掘,特征筛选,自动分箱
scikit-learn compatible tools for building credit risk acceptance models
Curated list of project-based tutorials
beerbottle / chatgpt-prompts
Forked from bytearch/chatgpt-promptsChatGPT中文Prompt提示词,常用、高频集合
beerbottle / jsoncrack.com
Forked from AykutSarac/jsoncrack.com✨ Innovative and open-source visualization application that transforms various data formats, such as JSON, YAML, XML, CSV and more, into interactive graphs.
Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.
Practice your pandas skills!
List of Data Science Cheatsheets to rule the world
《智能风控实践指南:从模型、特征到决策》代码。 《智能风控实践指南:从模型、特征到决策》书籍配套Python代码。
Feature selector is a tool for dimensionality reduction of machine learning datasets
A High-level Scorecard Modeling API | 评分卡建模尽在于此
My blogs and code for machine learning. http://cnblogs.com/pinard
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现
温州大学《机器学习》课程资料(代码、课件等)
beerbottle / toad
Forked from amphibian-dev/toadESC Team's scorecard tools
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
Solutions for Introduction to Programming Using Python by Daniel Liang.
beerbottle / xgboost
Forked from dmlc/xgboostScalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
把Python知识点整理成100道习题,知识点来自两本书:Python基础教程(第3版)和流畅的Python,以后会定期加入更多的习题,大家帮忙点个赞哈,点赞越多,更新越快~
🦕 常用正则大全, 支持web / vscode / idea / Alfred Workflow多平台
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)