Research on Promoting Differences for Machine Learning
December 02, 2019 09:13 Source: "China Social Sciences", December 2, 2019, Issue 1827, author: Reporter Hou Li

 Bet365 lotto review November 25,Professor Jennie Brand, a professor of sociology and statistics at the University of California, Los Angeles,The theme of "Use machine learning to reveal the heterogeneity of colleges and universities" at the Kennedy Government College of Harvard University,In -depth analysis of the current status of machine learning and promoting role in scientific research。

Bride said,In sociological research,race、Variables such as gender and socioeconomic status will cause people to have differences when evaluating the results of college talents。Usually,Sociologists have a certain degree of errors and prejudices in scientific research analysis,Sometimes it is difficult to avoid researchers' expectations and personal preferences during the research process。But,Data -driven machine learning can help scientific researchers discover the Bet365 app download research samples and variable factors that are easy to ignore before,Make the research results more comprehensive and accurate。

Specific speaking,The research team led by Blaide mainly uses the "cause and effect tree" computer model to classify the sample,Then different variables、Analysis of different attribution methods。Scientific researchers take "the impact of college brands on the salary of graduates" as an example,Analysis of social and economic differences caused by education level。During the research process,In addition to focusing on bet365 Play online games factors such as students' majors and education levels,Also focus on race、Gender、Different analysis and evaluation of family factors。

Blaide emphasized,If the data observed in sociological research is combined with the "cause and effect tree",It may change important changes in the results of the research。Machine learning as a calculation and statistical method,The mode and method of extracting data will play an important supplement and booster effect on traditional sociological research。

Editor in charge: Zhang Yueying
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