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Outlier Rejection

Christoffer Björkskog 0

To detect and get rid of outliers in a dataset (which may for instance have been caused by sensor error or data entry error) you first train your data, and remove the data point that has the highest residual error (over 10%) and then train again.

Otherwise erroneous data entries may give you an incorrect regression line.

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