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Tree ensemble
Classification Education Cross validation Hillclimbing Parameter optimization Stable model Random Forest E-learning Intermediate
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    Workflow
    Cross validation example
    Tree ensemble Classification Cross validation
    +1
    This example shows how to calculate non-standard evaluation metrics on each fold and then estimate fluctuation of the performance…
    lisovyi > Public > Examples > Cross Validation example
    1
    lisovyi
  2. Go to item
    Workflow
    06_Random_Forest
    E-learning Classification Tree ensemble
    +2
    E-learning course exercise. Train a Random Forest model to predict letters based on their image characteristics.
    stervis > Public > E-Learning > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > Exercises > 06_Random_Forest
    0
    stervis
  3. Go to item
    Workflow
    06_Random_Forest - Solution
    E-learning Classification Tree ensemble
    +2
    Solution to an e-learning course exercise. Train a Random Forest model to predict letters based on their image characteristics.
    stervis > Public > E-Learning > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > Solutions > 06_Random_Forest - Solution
    0
    stervis
  4. Go to item
    Workflow
    Cross validation example
    Tree ensemble Classification Cross validation
    +1
    This example shows how to calculate non-standard evaluation metrics on each fold and then estimate fluctuation of the performance…
    vittoriohaardt > Public > Cross Validation example
    0
    vittoriohaardt
  5. Go to item
    Workflow
    06_Random_Forest
    E-learning Classification Tree ensemble
    +2
    E-learning course exercise. Train a Random Forest model to predict letters based on their image characteristics.
    burdhasp > Public > Exercises - L2-DS KNIME Analytics Platform for Data Scientists - Advanced > Exercises > 06_Random_Forest
    0
    burdhasp
  6. Go to item
    Workflow
    Advanced Data Mining
    Tree ensemble Classification Parameter optimization
    +4
    Exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluating the performa…
    martyna > Training > L2-DS Training > exercises > 05. Advanced Data Mining
    0
    martyna
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    Workflow
    Advanced Data Mining
    Tree ensemble Classification Parameter optimization
    +4
    Exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluating the performa…
    tenalf > Public > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > exercises > 09. Advanced Data Mining
    0
    tenalf
  8. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    martyna > Training > L2-DS Training > solutions > 05. Advanced Data Mining - solution
    0
    martyna
  9. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    tenalf > Public > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > solutions > 09. Advanced Data Mining - solution
    0
    tenalf
  10. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    lilipertiwi > Public > KNIMEUserTraining > solutions > 09. Advanced Data Mining - solution
    0
    lilipertiwi
  11. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    ADILT22 > Course Material - Codefree Introduction to Data Science > VL10 - Ensemble > Workflows > parameter optimization demo
    0
    ADILT22
  12. Go to item
    Workflow
    Advanced Data Mining
    Tree ensemble Classification Parameter optimization
    +4
    Exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluating the performa…
    lilipertiwi > Public > KNIMEUserTraining > exercises > 09. Advanced Data Mining
    0
    lilipertiwi
  13. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    kzhqtt > Public > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > WA_Fn-UseC_-IT-Help-Desk > Advanced Data Mining
    0
    kzhqtt
  14. Go to item
    Workflow
    Cross validation example
    Tree ensemble Classification Cross validation
    +1
    This example shows how to calculate non-standard evaluation metrics on each fold and then estimate fluctuation of the performance…
    etayaa > Public > EDM_2022 > Modelo_Learning_Analytics_2022
    0
    etayaa
  15. Go to item
    Workflow
    Parameter Optimization and Cross Validation
    Tree ensemble Classification Parameter optimization
    +4
    There has been no description set for this workflow's metadata.
    julian.bunzel > Public > Small examples > Parameter Optimization and Cross Validation
    0
    julian.bunzel
  16. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    ADILT22 > Course Material - Codefree Introduction to Data Science > VL10 - Ensemble > Workflows > cross validation demo
    0
    ADILT22
  17. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    kzhqtt > Public > WA_Fn-UseC_-IT-Help-Desk > Advanced Data Mining
    0
    kzhqtt
  18. Go to item
    Workflow
    06_Random_Forest - Solution
    E-learning Classification Tree ensemble
    +2
    Solution to an e-learning course exercise. Train a Random Forest model to predict letters based on their image characteristics.
    burdhasp > Public > Exercises - L2-DS KNIME Analytics Platform for Data Scientists - Advanced > Solutions > 06_Random_Forest - Solution
    0
    burdhasp
  19. Go to item
    Workflow
    20210628 Pikairos Saving all models when using X-Partitioner
    Model Writer X-Partitioner Loop End
    +4
    This workflow shows how to combine a -X-Partitioner- node to a -Loop End- node to individually gather scoring information and mod…
    pikairos > Public > 20210629 Pikairos Saving all models when using X-Partitioner
    0
    pikairos
  20. Go to item
    Workflow
    Advanced Data Mining - Solution
    Tree ensemble Classification Parameter optimization
    +4
    Solution to the exercise 10 for KNIME User Training - Training a Random Forest model to predict a nominal target column - Evaluat…
    knime > Education > Courses > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > solutions > 09. Advanced Machine Learning - solution
    0
    knime

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