Hub
NodeNode / Visualizer

Labeling View

This node allows the user to manually assign labels to objects. This is useful to e.g. create training data for learning algorithms or create a ground-truth to test your algorithms etc. New labels can be created, renamed or deleted freely. NOTE: "?" is a reserved label and cannot be added. Exsiting labels of "?" will be replaced as Missing Values in the output table.

Node details

Input ports
  1. Data Type: Data
    Table
Output ports
  1. Annotated data Type: Data
    Annotated table

Related workflows & nodes

  1. Active Learning - Uncertainty Sampling
    active learning legacy uncertainty sampling +6
    This workflows shows examples of Active Learning with Uncertainty Sampling. Uncertainty sampling uses the model that is…
    knime > Examples > 04_Analytics > 12_Active_Learning > 03_Active_Learning_Uncertainty_Sampling
  2. Active Learning - Exploration / Exploitation Score
    PBAC active learning human-in-the-loop +13
    This workflows shows examples of Active Learning with the Exploration vs Exploitation strategy taken from Cebron et al,…
    knime > Examples > 04_Analytics > 12_Active_Learning > 01_Active_Learning_PBCA_default
  3. Active Learning - Exploration / Exploitation Score
    PBAC active learning human-in-the-loop +13
    This workflows shows examples of Active Learning with the Exploration vs Exploitation strategy taken from Cebron et al,…
    noyankara > Public > 01_Active_Learning_PBCA_default
  4. Active Learning for Document Classification
    active learning human-in-the-loop guided analytics +18
    This workflow defines a fully automated web based application that will label your data using active learning. The work…
    knime > Examples > 50_Applications > 57_Guided_Labeling > 02_Active_Learning_for_Document_Classification
  5. Guided Labeling for Document Classification
    active learning human-in-the-loop guided analytics +26
    This workflow defines a fully automated web based application that will label your data using active learning and weak …
    knime > Examples > 50_Applications > 57_Guided_Labeling > 01_Guided_Labeling_for_Document_Classification