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.
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Random Forest Learner4 %- Loop End (Column Append)3 %
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CSV Writer2 %- CSV Writer2 %
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- GroupBy2 %
Scorer2 %Tree Ensemble Learner2 %Tree Ensemble Predictor2 %- Loop End2 %
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- Table to H2O2 %
- Columns to Collection1 %
Database Writer1 %- Decision Tree Learner1 %
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Gradient Boosted Trees Learner1 %Random Forest Predictor1 %- Random Forest Predictor1 %
Row Splitter (deprecated)1 %Row Filter (deprecated)1 %- Normalizer1 %
- Table Partitioner1 %
- Target Shuffling1 %
- Workflow Executor1 %
XGBoost Tree Ensemble Learner (Regression)1 %- Anonymity Assessment1 %
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XLS Writer1 %- Weka Predictor (3.7)1 %
String to Date&Time1 %Table Writer< 1 %- Java Snippet< 1 %
Naive Bayes Predictor< 1 %Numeric Scorer< 1 %- Numeric Scorer< 1 %
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- Cell Splitter< 1 %
Linear Correlation< 1 %- Missing Value Column Filter< 1 %
Column Splitter< 1 %- End IF< 1 %
Naive Bayes Learner< 1 %- Constant Value Row Appender< 1 %
- Column Combiner< 1 %
- Number to Category (Apply)< 1 %
- Double to Integer< 1 %
Joiner< 1 %- Low Variance Filter< 1 %
- Normalizer Apply (PMML)< 1 %
- String Manipulation (Multi Column)< 1 %
- Table Difference Finder< 1 %
- Math Formula< 1 %
Excel Writer (XLS)< 1 %XGBoost Tree Ensemble Learner< 1 %- Fingerprints< 1 %
- Split Collection Column< 1 %
- Catch Errors (Data Ports)< 1 %
Table Row to Variable< 1 %Table Row To Variable Loop Start< 1 %Backward Feature Elimination Start (1:1)< 1 %Loop End< 1 %Counting Loop Start< 1 %- Column List Loop Start< 1 %
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- Recursive Loop End< 1 %
Recursive Loop Start< 1 %k-Means (deprecated)< 1 %- MultiLayerPerceptron Predictor< 1 %
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PCA Compute< 1 %- Linear Regression Learner< 1 %
Logistic Regression Learner< 1 %- Logistic Regression Predictor< 1 %
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Tree Ensemble Learner< 1 %Gradient Boosted Trees Predictor< 1 %Gradient Boosted Trees Predictor< 1 %- Gradient Boosted Trees Predictor (Regression)< 1 %
- Tree Ensemble Learner< 1 %
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- Random Forest Learner (Regression)< 1 %
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Concatenate (Optional in)< 1 %Auto-Binner< 1 %Cell Replacer< 1 %- Cell Splitter By Position< 1 %
- Column Auto Type Cast< 1 %
- Column Comparator< 1 %
- Category to Number< 1 %
- Number to String< 1 %
String To Number< 1 %- String to Number< 1 %
Column Appender< 1 %- Column Renamer (Dictionary)< 1 %
- Column Merger< 1 %
Constant Value Column< 1 %- Correlation Filter< 1 %
- Table Validator< 1 %
- Domain Calculator< 1 %
- Edit Nominal Domain< 1 %
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- Extract Missing Value Cause< 1 %
- Reference Column Filter< 1 %
- Reference Column Splitter< 1 %
- Nominal Value Row Filter< 1 %
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- Many to One (PMML)< 1 %
- Missing Value< 1 %
Column Rename< 1 %- Number Rounder< 1 %
- RowID< 1 %
- String Manipulation< 1 %
- String Manipulation (Variable)< 1 %
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