X-Partitioner

LoopStart

This node is the first in a cross validation loop. At the end of the loop there must be a X-Aggregator to collect the results from each iteration. All nodes in between these two node are executed as many times as iterations should be performed.

Input Ports

  1. Type: Data
    The datatable that is to be split

Output Ports

  1. Type: Data
    The data table with the training data
  2. Type: Data
    The data table with the test data

  1. Feature selection simulation

    Users > kienerj

  2. Credit Scoring

    Credit Scoring Credit Rating Customer Risk Decision Tree Neural Network SVM
    This KNIME workflow focuses on creating a credit scoring model based on historical data. As with all data mining modelin…
    Users > knime > Examples > 50_Applications > 02_Credit_Scoring

  3. Housing Value Prediction using XGBoost for Regression

    XGBoost regression gradient boosting gradient boosted trees parameter optimization cross validation machine learning data mining
    This workflow shows how the XGBoost nodes can be used for regression tasks. It also demonstrates a combination of parame…
    Users > shima0011 > 04_Analytics > 16_XGBoost

  4. Analytics - Model Selection to Predict Flight Departure Delays

    data science machine learning model selection data preparation ETL airline data set
    This workflow trains a number of data analytics models and automatically selects the best model to predict departure del…
    Users > knime > Examples > 50_Applications > 28_Predicting_Departure_Delays

  5. dec_tree

    Users > knime > Examples > 50_Applications > 36_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation > Models > classification

  6. Anonymize Personal Data

    GDPR Customer Intelligence
    This example shows one way of anonymizing data. it uses the approved adults data set. For this example, distance matrix …
    Users > knime > Examples > 50_Applications > 34_GDPR_examples

  7. Cross Validation with SVM

    A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base learning algorithm.
    Users > shima0011 > 04_Analytics > 11_Optimization

  8. Cross Validation with SVM and Parameter Optimization

    A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base learning algorithm.
    Users > shima0011 > 04_Analytics > 11_Optimization

  9. Cross Validation with SVM

    A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base learning algorithm.
    Users > aajaradata

  10. h2o_glm

    Users > knime > Examples > 50_Applications > 36_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation > Models > classification

Extension

This node is part of the extension

KNIME Core

 v4.0.0

Short Link

Drag node into KNIME Analytics Platform