首页|Studies from Federal Reserve Board in the Area of Machine Learning Reported (Mis sing Values Handling for Machine Learning Portfolios)
Studies from Federal Reserve Board in the Area of Machine Learning Reported (Mis sing Values Handling for Machine Learning Portfolios)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news originating from Washington, District of Columbia, by NewsRx correspondents, research stated, “We characterize the st ructure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with crosssectional means performs well compared to rigorous e xpectation -maximization methods.” Our news journalists obtained a quote from the research from Federal Reserve Boa rd, “This stems from three facts about predictor data: (1) missingness occurs in large blocks organized by time, (2) crosssectional correlations are small, and (3) missingness tends to occur in blocks organized by the underlying data sourc e. As a result, observed data provide little information about missing data.”
WashingtonDistrict of ColumbiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Lear ningFederal Reserve Board
NETL
NSTL
