IB Computer Science Topic A4.2.1: Significance of Data Cleaning Key Definitions

The stage of data preprocessing where errors, gaps, duplicates and inconsistencies are removed from a data set before a model is trained.

Filling a missing value with an estimate calculated from the data that is present, most often the mean, median or mode of the same column.

Rescaling a numeric feature into a fixed range, usually 0 to 1, so that features measured in different units contribute to a model on a comparable scale. This is a different process from normalization in relational database design.

A data point whose value sits far away from the rest of the observations in its column, which can pull a model's fit toward a value that is not typical of the data.

Rescaling a numeric feature so that it has a mean of 0 and a standard deviation of 1, which expresses every value as the number of standard deviations it sits from the mean.