Target Shuffling

This node performs Target Shuffling by randomly permuting the values in one column of the input table. This will break any connection between input variables (learning columns) and response variable (target column) while retaining the overall distribution of the target variable. Target shuffling is used to estimate the baseline performance of a predictive model. It's expected that the quality of a model (accuracy, area under the curve, R², ...) will decrease drastically if the target values were shuffled as any relationship between input and target was removed. It's advisable to repeat this process (target shuffling + model building + model evaluation) many times and record the bogus result in order to receive good estimates on how well the real model performs in comparison to randomized data. Target shuffling is sometimes called randomization test or y-scrambling. For more information see also Handbook of Statistical Analysis and Data Mining Applications by Gary Miner, Robert Nisbet, John Elder IV.

Options

Column to shuffle

Select the column whose values should be randomly shuffled. This breaks the relationship between this column and other columns in the table, which is useful for creating negative controls in machine learning experiments.

Use seed

When enabled, the shuffling will use a fixed seed value, making the randomization reproducible across multiple executions. When disabled, each execution will produce different random shuffling results.

Random seed

The seed value used for random number generation. Use the same seed to get identical shuffling results across multiple executions. You can enter a custom value or use the random seed generation button below.

Draw seed

Generate a random seed and set it in the Random seed input above for reproducible runs.

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