NodePartitioning

Manipulator

The input table is split into two partitions (i.e. row-wise), e.g. train and test data. The two partitions are available at the two output ports. The following options are available in the dialog:

Input Ports

  1. Port Type: Data
    Table to partition.

Output Ports

  1. Port Type: Data
    First partition (as defined in dialog).
  2. Port Type: Data
    Second partition (remaining rows).

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    aajaradata > 03_Learning_a_Tree_Ensemble_Model

  2. Variable Importance

    This workflow calculates how important each variable is for a correct classification.
    aajaradata > 04_Meassuring_Variable_Importance

  3. My_First_Project_Home

    djibrina-barry > My_First_Project_Home

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    shima0011 > 04_Analytics > 10_Scoring > 02_Optimizing_Classification_Threshold_Based_on_Profit

  5. Naive Bayes

    A simple example using a Naive Bayes learner and predictor to classify some shuttle data. For more background informatio…
    shima0011 > 04_Analytics > 04_Classification_and_Predictive_Modelling > 03_Example_for_Learning_a_Naive_Bayes_Model

  6. Logistic Regression

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    This workflow is an example of how to build a basic prediction / classification model using logistic regression.
    shima0011 > 04_Analytics > 04_Classification_and_Predictive_Modelling > 06_Logistic_Regression

  7. How to use the Simple Regression Tree

    This workflow illustrates how to use the Simple Regression Tree nodes to predict the value of a numerical target column.
    shima0011 > 04_Analytics > 05_Regressions > 01_Learning_a_Simple_Regression_Tree

  8. Semantic Segmentation with Deep Learning in KNIME

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    shima0011 > 04_Analytics > 14_Deep_Learning > 02_Keras > 06_Semantic_Segmentation

  9. Sentiment Analysis

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    shima0011 > 04_Analytics > 14_Deep_Learning > 02_Keras > 07_Sentiment_Analysis_with_Deep_Learning

  10. Training a Churn Predictor

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    dainq > Churn Prediction > Building a Churn Prediction Model