The data table at this out-port is derived from the data input at in-port #0 and can have a number of options. It will always contain:

The ground truth column selected in the dialog.
The chosen model-prediction columns chosen in the dialog.
A boolean "selected" column with a value of "true" or "false" depending on whether or not the row was selected in the confusion matrix in the view. The value will be "missing" if no selections were made or the view was not opened.

Optionally, the data table may also contain:

A String-value column for EACH of the model-prediction columns chosen in the dialog. Each column will contain the label (either positive class or negative class) depending on the threshold for that model. The thresholds will either be taken from the dialog "Threshold Method" option OR be taken from the most recent value saved from the view for that model. To see which threshold was used to label each column, reference the row of statistics relating to each model in the 2nd out-port. To enable this option select the "Append new predictions for all models" option in the dialog.
A single String-value column for the selected model in the view. This column will contain the label (either positive class or negative class) depending on the threshold for the selected model in the view. This column will contain "missing values" if no model was selected in the view. The name of this column can also be set in the dialog to assist in downstream workflow processes. The selected model column name and threshold will also be output as flow variables from this node.
To enable this option select the "Append prediction column for the selected model" option in the dialog.

The final option for this table is to exclude tables that are not the ground truth column or the selected model-prediction column(s). This will filter out any unwanted columns which may have been left over from upstream predictors. This option is NOT enabled by default. In order to take advantage of this option DESELECT the "Retain non-prediction columns in output table" option in the dialog.

KNIME for Data Visualization

Visualizations by Use Case

Presentation

Example for Pie Chart

Example for Bar Chart

Sunburst Chart Examples

Example for Heatmap

Example for Stacked Area Chart

How to Create a Scatter plot

Sunburst Chart Examples

Scatter Plot Examples

Example for Histogram

Exploration and Statistical Analysis

Example for Density Plot

Hierarchical Clustering

Parallel Coordinates Plot Examples

Simple Example with Statistics

Scatter Plot Matrix Examples

Comparing Distributions

Simple tag cloud and network

Example for Box Plot

Time Series Visualizations

Example for Line Plot

Multiple Lines Plot

Animated Bar Chart

Inspect and Remove Seasonality

Text Visualizations

Text Visualization

Named Entity Tag Cloud

Geospatial Visualizations

KNIME for Geospatial Analysis

Model scoring

ROC Curve Examples

Evaluating Classification Model Performance

Visual Scoring Techniques for Classification Models

Binary Classification Inspector

Evaluating Regression Model Performance

Color, Formats and Utility Nodes

Dataset cleaning and formatting

String Cleaner Example

Write images to Excel

Sales Apportionment - Data App

Code-based Visualizations

Visualisation with Python View (Matplotlib and Seaborn Packages)

Simple R View

Sankey Chart with Echarts example

Radar Chart with Echarts example

Pairs Plot Using R

Custom Javascript Visualization Example

Composite and Interactive Visualizations

Components & Composite Views

Adding Interactive Filtering

Interactive Dashboard Netflix Shows

Interactive Dashboard Netflix Shows Advanced

Interactive Visualizations

Refresh Button Widget Example Sequential Logic

Column Selection Widget Re-execution

Refresh Button Widget Example: Dynamic Content

Refresh Button Widget Example: Dynamic Node Settings

Nominal Row Filter Widget Re-execution

Column Filter Widget Re-execution

Single Selection Widget Re-execution

Tag Genes in Disease related Literature

Other visualizations

Reporting Minimal Example

Image Dataset Visual Filtering