Conformal Partitioning

The input table is split into two partitions for calibration and train data (i.e. row-wise). The difference to a normal partitioning node is that the calibration set is adjusted to: (largest lower value divisible by 100) -1. The two partitions are available at the two output ports. The following options are available in the dialog:

Options

Absolute

Specify the absolute number of rows in the calibration set (before adjustment). 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 calibration set (before adjustment). It must be between 0 and 100, inclusively.

Take from top

This mode puts the top-most rows into the calibration set and the remainder in the training set.

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).

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 fixed seed here in order to get reproducible results upon re-execution. If you do not specify a seed, a new random seed is taken for each execution.

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Workflows

• Conformal prediction classificationKNIME Hub
• Conformal_regression_by_RedfieldKNIME Hub
• COPA 2023 - Conformal predictive systems - simple use casesKNIME Hub
• COPA 2023 - Mondrian conformal predictive systems experimentKNIME Hub
• JKISeason2-25KNIME Hub
• Show all 7 workflows

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