摘要
该教学实验设计以Python的机器学习整合包为内核,以煤岩瓦斯复合动力灾害预测为背景,使用Python语言编写,方便了学生在有限的教学与实验课程中实现机器学习挖掘数据的编程任务。实验内容包括数据集构建、Apis调用、数据集读取、数据归一化处理、模型训练与导出、样本集预测、模型准确度检验等环节。该教学实验涉及学科交叉,实用性强,能够为提升学生使用恰当的现代化分析技术与工具的能力。
The teaching experiment design takes Python’s machine learning integration package as the core,takes coal,rock and gas composite dynamic disaster prediction as the background,and is written in Python language,which facilitates students to realize the programming task of machine learning and mining data in limited teaching and experiment courses.The experiment contents include data set construction,Apis call,data set reading,data normalization processing,model training and export,sample set prediction,model accuracy test and other links.This teaching experiment involves interdisciplinary subjects and has strong practicability,which can improve students’ability to use appropriate modern analysis technology and tools.
作者
杜锋
汪博威
汪奥杰
王凯
王玮
DU Feng;WANG Bowei;WANG Aojie;WANG Kai;WANG Wei(School of Emergency Management and Safety Engineering,China University of Mining&Technology(Beijing),Beijing 100083,China;School of Cyber Science and Engineering,Southeast University,Nanjing 211102,China)
出处
《实验技术与管理》
CAS
北大核心
2023年第4期181-186,共6页
Experimental Technology and Management
基金
国家自然科学基金项目(52130409,52004291)
全国煤炭行业高等教育教学改革研究课题(2021MXJG028)
中国矿业大学(北京)课程建设与教学改革项目(J221203)。
关键词
实验教学改革
安全大数据
机器学习
数据挖掘
灾害预测
experimental teaching reform
security big data
machine learning
data mining
disaster prediction
