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Missing value imputation for predictive models

机译:预测模型的缺失值估算

摘要

Provided are techniques for imputing a missing value for each of one or more predictor variables. Data is received from one or more data sources. For each of the one or more predictor variables, an imputation model is built based on information of a target variable; a type of imputation model to construct is determined based on the one or more data sources, a measurement level of the predictor variable, and a measurement level of the target variable; and the determined type of imputation model is constructed using basic statistics of the predictor variable and the target variable. The missing value is imputed for each of the one or more predictor variables using the data from the one or more data sources and one or more built imputation models to generate a completed data set.
机译:提供用于为一个或多个预测变量中的每一个估算缺失值的技术。从一个或多个数据源接收数据。对于一个或多个预测变量中的每一个,根据目标变量的信息建立归因模型。根据一个或多个数据源,预测变量的测量水平和目标变量的测量水平,确定要构建的插补模型的类型。并使用预测变量和目标变量的基本统计数据来构造确定的插补模型类型。使用来自一个或多个数据源的数据和一个或多个构建的插补模型为一个或多个预测变量的每一个估算缺失值,以生成完整的数据集。

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