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XGBoost
Knime Automl H2o H2o.ai Learning Machine Auto Python
  1. Workflow
    H2O.ai AutoML (wrapped with Python) with vtreat data preparation in KNIME for classification problems (with R vtreat)
    H2o Automl Knime
    +10
    H2O.ai AutoML (wrapped with Python) with vtreat data preparation in KNIME for classification problems (with R vtreat) - a powerfu…
    mlauber71 > Public > automl > kn_automl_h2o_classification_python_vtreat
    0
    mlauber71
  2. Workflow
    Forest Cover Type Classification with XGBoost
    XGBoost Classification Gradient boosting
    +7
    This workflows shows how the XGBoost nodes can be used for classification tasks.
    knime > Examples > 04_Analytics > 16_XGBoost > 01_Classify_Forest_Covertypes_with_XGBoost
    0
    knime
  3. Workflow
    LIME Loop Nodes with a Custom Model
    LIME Machine learning interpretability Mli
    +9
    This is an example for computing explanation using LIME. An XGBoost model was picked, but any model and its set of Learner and Pr…
    knime > XAI Space > Classification > Custom Models > 07_Compute_LIMEs
    0
    knime
  4. Workflow
    Local Explanation View Component with a Custom Model
    XAI Counterfactual Integrated Deployment
    +10
    This workflow demonstrates the use of the Local Explanation View component on three separate datasets on which the different cust…
    knime > XAI Space > Classification > Custom Models > 01_Local_Explanation
    0
    knime
  5. Workflow
    Mixing Deep Learning with XGBoost
    Deep Learning Machine Learning XGBoost
    +11
    This workflow shows how to train an XGBoost based image classifier that uses a pretrained convolutional neural network to extract…
    lyudmila > Public > Mixing_DL_with_XGBoost
    0
    lyudmila
  6. Workflow
    Mixing Deep Learning with XGBoost
    Deep Learning Machine Learning XGBoost
    +11
    This workflow shows how to train an XGBoost based image classifier that uses a pretrained convolutional neural network to extract…
    yusupov > Public > Mixing_DL_with_XGBoost
    0
    yusupov
  7. Workflow
    Mixing Deep Learning with XGBoost
    Deep Learning Machine Learning XGBoost
    +11
    This workflow shows how to train an XGBoost based image classifier that uses a pretrained convolutional neural network to extract…
    christian.birkhold > Deep Learning with XGBoost > Mixing_DL_with_XGBoost
    1
    christian.birkhold
  8. Workflow
    Mixing_DL_with_XGBoost
    Deep Learning Machine Learning XGBoost
    +11
    This workflow shows how to train an XGBoost based image classifier that uses a pretrained convolutional neural network to extract…
    nemad > Public > Mixing_DL_with_XGBoost
    0
    nemad
  9. Workflow
    XAI View Component with a Custom Model
    Guided analytics Integrated deployment XAI
    +7
    This application is a simple example of Xgboost model with KNIME Software for binary and multiclass classification. The model out…
    knime > XAI Space > Classification > Custom Models > 02_Explainable_Artificial_Intelligence_(XAI)
    0
    knime
  10. Workflow
    H2O.ai AutoML (wrapped with R) with vtreat data preparation in KNIME for classification problems (with R vtreat)
    H2o Automl Knime
    +10
    H2O.ai AutoML (wrapped with R) in KNIME for classification problems (with R vtreat) - a powerful auto-machine-learning framework …
    mlauber71 > Public > automl > kn_automl_h2o_classification_r_vtreat
    0
    mlauber71
  11. Workflow
    (forum example) H2O.ai AutoML (wrapped with R) with vtreat data preparation in KNIME for classification problems (with R vtreat)
    H2o Automl Knime
    +10
    H2O.ai AutoML (wrapped with R) with vtreat data preparation in KNIME for classification problems (with R vtreat) - a powerful aut…
    mlauber71 > Public > forum > kn_forum_automl_h2o_classification_r_vtreat_svm
    0
    mlauber71
  12. Workflow
    H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework
    H2o Automl Knime
    +8
    H2O.ai AutoML (generic KNIME nodes) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hu…
    mlauber71 > Public > automl > kn_automl_h2o_classification
    0
    mlauber71
  13. Workflow
    H2O.ai AutoML (wrapped with R) in KNIME for classification problems
    H2o Automl Knime
    +3
    H2O.ai AutoML (wrapped with R) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hub.kni…
    mlauber71 > Public > automl > kn_automl_h2o_classification_r
    0
    mlauber71
  14. Workflow
    H2O.ai AutoML (wrapped with Python) in KNIME for classification problems
    H2o Automl Knime
    +1
    H2O.ai AutoML (wrapped with Python) in KNIME for classification problems - a powerful auto-machine-learning framework (https://hu…
    mlauber71 > Public > automl > kn_automl_h2o_classification_python
    0
    mlauber71
  15. Workflow
    Model Selection with Integrated Deployment
    Chemistry Naive bayes Random forest
    +11
    This workflow deploys an advanced parameter optimzation protocol with four machine learning methods. In this implementation the c…
    knime > Education > Courses > L4-CA Machine Learning for Chemical Applications > Exercises > 02_Hyperparameter Optimization
    0
    knime
  16. Workflow
    Model Selection with Integrated Deployment
    Chemistry Naive bayes Random forest
    +11
    This workflow deploys an advanced parameter optimzation protocol with four machine learning methods. In this implementation the c…
    knime > Education > Courses > L4-CA Machine Learning for Chemical Applications > Solutions > 02_Hyperparameter Optimization
    0
    knime
  17. Workflow
    Model Selection with Integrated Deployment
    Chemistry Naive bayes Random forest
    +11
    This workflow deploys an advanced parameter optimzation protocol with four machine learning methods. In this implementation the c…
    knime > Education > Courses > L4-CA Machine Learning for Chemical Applications > Solutions > 02_Hyperparameter Optimization_Bonus
    0
    knime
  18. Workflow
    Model Selection with Integrated Deployment
    Chemistry Naive bayes Random forest
    +11
    This workflow deploys an advanced parameter optimzation protocol with four machine learning methods. In this implementation the c…
    knime > Life Sciences > Events > 2020_10_Integrated_Deployment_In_Action_Webinar > Model Selection with Integrated Deployment
    0
    knime
  19. Workflow
    H2O.ai AutoML (wrapped with Python) in KNIME for classification problems
    H2o Automl Knime
    +1
    H2O.ai AutoML in KNIME for classification problems a powerful auto-machine-learning framework https://hub.knime.com/mlauber71/spa…
    mlauber71 > Public > kn_example_bigdata_h2o_automl_spark > s_410_h2o_automl_classification_python
    0
    mlauber71
  20. Workflow
    Compute Local Model-agnostic Explanations (LIMEs)
    LIME Machine learning interpretability Mli
    +9
    This is an example for computing explanation using LIME. An XGBoost model was picked, but any model and its set of Learner and Pr…
    knime > Examples > 04_Analytics > 17_Machine_Learning_Interpretability > 01_Compute_LIMEs
    1
    knime