Partitioning

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

Options

Absolute

Specify the absolute number of rows in the first partition. If there are less rows than specified here, all rows are entered into the first table, while the second table contains no rows.

Relative

The percentage of the number of rows in the input table that are in the first partition. It must be between 0 and 100, inclusively.

Take from top

This mode allows you to specify the number of top-most rows to be put into the first output table, with the remainder going into the second table.

Linear sampling

This mode always includes the first and the last row and selects the remaining rows linearly over the whole table (e.g. every third row). This is useful to downsample a sorted column while maintaining minimum and maximum value.

Draw randomly

Random sampling of all rows, you may optionally specify a fixed seed (see below).

Stratified sampling

Check this button if you want stratified sampling, i.e. the distribution of values in the selected column is (approximately) retained in the output tables. You may optionally specify a fixed seed (see below).

Use random seed

If either random or stratified sampling is selected, you may enter a seed to ensure reproducible results upon re-execution. Entering a seed makes row selection random but fixed, meaning the same rows will be selected each time. If you do not specify a seed different rows will be selected each time.

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Links

• Splitting data into partsby mlauber71 on 2019-01-01

  forum.knime.com/p/52266
• How to do Data analysis with knimeby armingrudd on 2019-10-21

  forum.knime.com/p/72844
• Table random manipulationsby armingrudd on 2019-08-20

  forum.knime.com/p/68248
• Row-based filter, extract every X:th rowby aworker on 2023-02-06

  forum.knime.com/p/192385
• scorer does not shows the total number of instancesby qqilihq on 2022-02-17

  forum.knime.com/p/148230
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