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This data transformation produces a loss of information with regards to the original dataset which affects the utility of masked data, so, the aim of microaggregation algorithms is to find the partition that minimises the information loss while ensuring a certain level of privacy.
However, we want to note that the main conclusions of the modeling are not dependent on this data transformation.
Because there is no internal standard when using UV-LDI mass spectrometry, this data transformation is necessary in order to carry out multivariate statistical analysis [7].
A reasonable alternative is to transform all cell frequency measurements using the arcsine-square-root [18], and the results we present here are generated using this data transformation.
For parameter estimation the measured areas A for CLV3 and WUS were transformed to volumes V assuming a spherical symmetry of the cell pools:
Therefore, the performance of the predictive algorithm was not affected by this data transformation step.
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To account for this, data transformations were conducted where appropriate, to stabilize the seasonal variations [ 13].
In this paper three different approaches were explored: 1. Implement the data transformation logic within the data pipeline.
Implement the data transformation logic within the data pipeline.
Venzee CEO Kate Hiscox sees this as a data transformation problem, something her company has been working to solve for the past three years in the retail space.
The interplay makes this process of data transformation more accessible to business data analysts, while increasing the productivity of quantitative data scientists who can use the human-machine interaction to get through various types of data in a faster manner.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com