I pasted the Statistics node into a description field of its own copy. Here’s the very long (thus cropped), JSON-like string result I get:
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It also illustrates the use of KNIME’s hiliting capabilities, which allow interactive views to be connected within the same workflow.\r\n\r\nTask Predict the income group from demographic attributes of the adult data set (census data).\r\n\r\nFind more information on KNIME’s Learning Hub at KNIME Learning | KNIME (tutorials, videos, white papers, many more workflows)”,“textAlignment”:“LEFT”,“width”:1021},“1”:{“bgcolor”:16777215,“borderColor”:16751466,“borderSize”:4,“defaultFontSize”:9,“height”:301,“location”:{“x”:40,“y”:200},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:24,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:148,“start”:24}],“text”:" Data Reading\r\n\r\nRead the adult data set file. There is one row for each person, plus demographic info and the income group. The file is located in TheData/Basics/“,“textAlignment”:“LEFT”,“width”:181},“2”:{“bgcolor”:16777215,“borderColor”:6992891,“borderSize”:4,“defaultFontSize”:9,“height”:301,“location”:{“x”:220,“y”:200},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:24,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:32,“start”:24}],“text”:” Graphical Properties\r\n\r\nAssign colors by income group.“,“textAlignment”:“LEFT”,“width”:161},“3”:{“bgcolor”:16777215,“borderColor”:16766976,“borderSize”:4,“defaultFontSize”:9,“height”:301,“location”:{“x”:380,“y”:200},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:24,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:95,“start”:24}],“text”:” Data Partitioning\r\n\r\nCreate two separate partitions from original data set: training set (80%) and test set (20%).“,“textAlignment”:“LEFT”,“width”:161},“4”:{“bgcolor”:16777215,“borderColor”:14020978,“borderSize”:4,“defaultFontSize”:9,“height”:301,“location”:{“x”:540,“y”:200},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:31,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:141,“start”:31}],“text”:” Train a Model\r\n\r\nThis node builds a decision tree. Other Learner nodes train other models. Most Learner nodes output a PMML model (blue square output port).“,“textAlignment”:“LEFT”,“width”:241},“5”:{“bgcolor”:16777215,“borderColor”:300888,“borderSize”:4,“defaultFontSize”:9,“height”:301,“location”:{“x”:780,“y”:200},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:20,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:89,“start”:20}],“text”:” Apply the Model\r\n\r\nPredictor nodes apply a specific model to a data set and append the model predictions. “,“textAlignment”:“LEFT”,“width”:141},“6”:{“bgcolor”:16777215,“borderColor”:14649867,“borderSize”:4,“defaultFontSize”:9,“height”:301,“location”:{“x”:920,“y”:200},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:19,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:113,“start”:19}],“text”:” Score the Model\r\n\r\nCompute a confusion matrix between real and predicted class values and calculate the related accuracy measures.“,“textAlignment”:“LEFT”,“width”:141},“7”:{“bgcolor”:16777215,“borderColor”:6992891,“borderSize”:4,“defaultFontSize”:9,“height”:200,“location”:{“x”:880,“y”:520},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:25,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:34,“start”:25}],“text”:” Visualize \r\n\r\nCreate interactive scatter plot.“,“textAlignment”:“LEFT”,“width”:181},“8”:{“bgcolor”:16777215,“borderColor”:6992891,“borderSize”:4,“defaultFontSize”:9,“height”:202,“location”:{“x”:220,“y”:519},“styles”:[{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:1,“length”:31,“start”:0},{“color”:0,“fontName”:“Arial”,“fontSize”:10,“fontStyle”:0,“length”:66,“start”:31}],“text”:” Descriptive Statistics \r\n\r\nCalculate the statistical properties of the data set 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z”}},“configType”:“ConfigMap”,“key”:“dateTimeFormats”},“yAxisMax”:{“configType”:“ConfigValueString”},“showWarningInView”:{“configType”:“ConfigValueBoolean”,“value”:true},“yAxisLabel”:{“configType”:“ConfigValueString”,“value”:“”},“subscribeFilter”:{“configType”:“ConfigValueBoolean”,“value”:true},“enableTitleChange”:{“configType”:“ConfigValueBoolean”,“value”:true},“enableXAxisLabelEdit”:{“configType”:“ConfigValueBoolean”,“value”:true},“reportOnMissingValues”:{“configType”:“ConfigValueBoolean”,“value”:true},“enablePanning”:{“configType”:“ConfigValueBoolean”,“value”:true},“snapToPoints”:{“configType”:“ConfigValueBoolean”,“value”:false},“showSelectedOnly”:{“configType”:“ConfigValueBoolean”,“value”:false},“gridColor”: cropped for “brevity”
Can you reproduce this? And irrespective of the format (JSON or not), earlier versions of KNIME didn’t do thi – they simply wouldn’t ever paste nodes into text boxes.