ming_hu_yong的博客

本文主要分享自己总结的关于 xgboost 的笔记。 基础知识 基学习器 CART Regression:L(yi,y^i)=(yi−f(xi))2Classification:Gini(D)=∑k=1Kpk(1−pk)=1−∑k=1Kpk2 Regression: L(y_i, \hat y_i) = (y_i - f(x_i))^2 \\ Classification: Gini(D) = \sum_{k=1}^K p_k(1-p_k) = 1 - \sum_{k=1}^K p_k^2 Regres

Datawhale 和天池联合举办了【零基础入门数据挖掘-心跳信号分类预测】的入门赛事，比赛链接： https://tianchi.aliyun.com/competition/entrance/531883/introduction 文章目录

Graph Fundamentals Basic concepts to get you going. A graph database can store any kind of data using a few simple concepts: Nodes - graph data records Relationships - connect nodes Properties - named data values A Graph Database Neo4j stores data in a G