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Abstract

An outlier removal method for removing an outlier included in training data including data of an explanatory variable and an objective variable used for machine learning is provided with an evaluation metrics calculation step comprising a model creation step of dividing the training data into teacher data and test data and creating a regression model representing a correlation between the explanatory variable and the objective variable using the teacher data, a first evaluation metrics calculation step of calculating an evaluation metric using the test data on the created regression model to provide a first evaluation metric, and a second evaluation metrics calculation step of calculating an evaluation metric using the teacher data on the created regression model to provide a second evaluation metric, and repeating the model creation step, the first evaluation metrics calculation step, and the second evaluation metrics calculation step for a predetermined number of times; an outlier determination step of determining an outlier by determining whether each data included in the training data is an outlier based on a calculation result of the evaluation metrics calculation step by using both the first evaluation metric and the second evaluation metric as metrics when the each data is used as the test data; and an outlier removal step of removing data determined to be an outlier in the outlier determination step.

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