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Random forest
Ensemble model Machine learning Parameter optimization Education Regression Classification Banking Internet of Things Analytics Decision Tree Ensemble
+4
  1. Workflow
    Model Interpretability, Titanic
    Titanic Random Forest ML Interpretability
    +6
    The workflow demonstrates how to use SHAP, Shapley Values and LIME implemenatations in KNIME 4.0 and generates a basic combined v…
    knime > Examples > 04_Analytics > 17_Machine_Learning_Interpretability > 03_Titantic_Prediction_Explanations
    4
    knime
  2. Workflow
    Taxi demand prediction training workflow
    Demand prediction Random forest Time series prediction
    +5
    In this use case, we will use the NYC taxi dataset and a Random Forest to train a simple time series prediction model to predict …
    knime > Examples > 10_Big_Data > 02_Spark_Executor > 11_Taxi_Demand_Prediction > Training_workflow
    4
    knime
  3. Workflow
    Taxi demand prediction deployment
    Demand prediction Random forest Time series prediction
    +5
    In this use case, we will use the NYC taxi dataset and a Random Forest to train a simple time series prediction model to predict …
    knime > Examples > 10_Big_Data > 02_Spark_Executor > 11_Taxi_Demand_Prediction > Deployment_workflow
    2
    knime
  4. Workflow
    Risk Scoring: Estimation of Probability of Default
    Risk Scoring Risk assessment
    +5
    The probability of default is a metric that lenders use to assess risk on customers, group them into similar risk bands, and dete…
    knime > Examples > 50_Applications > 38_Credit_Risk_Assessment > Credit_Scoring_Training
    2
    knime
  5. Workflow
    Overview of Credit Card Fraud Detection Techniques
    Credit card Fraud DBSCAN
    +8
    This workflow shows an overview of credit card fraud detection techniques. The performances of the techniques are evaluated on th…
    knime > Finance, Accounting, and Audit > Overview of Credit Card Fraud Detection Techniques
    2
    knime
  6. 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
    1
    knime
  7. Workflow
    Training a Random Forest
    Classification Machine learning Prediction
    +11
    Training a decision tree and training a random forest of decision trees.
    knime > Examples > 04_Analytics > 04_Classification_and_Predictive_Modelling > 09_Random_Forest
    1
    knime
  8. Workflow
    Random Forest, Gradient Boosted Trees, and TreeEnsemble
    Classification Machine learning Prediction
    +9
    This workflow solves a binary classification problem on the adult dataset using more advanced algorithms: - Random Forest - Gradi…
    rs1 > Public > Tree_Ensembles
    1
    rs1
  9. Workflow
    Applying Optimized Threshold from Binary Calssification Inspector
    Machine Learning Classification Data Mining
    +7
    This workflow is made to show how to apply the threshold computed by the Binary Classification Inspector for new data. the thresh…
    paolotamag > Public > Applying_Optimized_Threshold_from_Binary_Calssification_Inspector
    1
    paolotamag
  10. Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +5
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 02_Fraud_Detection_Deployment
    1
    knime
  11. Workflow
    Analyzing Churn Models with the Binary Classification Inspector
    Machine Learning Classification Data Mining
    +7
    This workflow demonstrates the functionality of the Binary Classification Inspector node. It produces a complex view made of four…
    knime > Examples > 04_Analytics > 04_Classification_and_Predictive_Modelling > 10_Analyzing_Churn_Models_with_the_Binary_Classification_Inspector
    1
    knime
  12. Workflow
    Training a Decision Tree
    Classification Machine learning Prediction
    +9
    Training a decision tree and training a random forest of decision trees. Adult.csv dataset describes US census information. Outpu…
    rs1 > Public > 09_Random_Forest
    1
    rs1
  13. Workflow
    Lean Restocking Alert Signal: Training
    Backward feature elimination Random forest Decision tree
    +7
    This workflow implements an alarm system for bycicle restocking at Washington bike stations. The dataset is the bike dataset. Pro…
    knime > Examples > 50_Applications > 55_Bike_Restocking_Alert > 01_Bike_Restocking_Alert_Training_w_Feature_Selection
    1
    knime
  14. Workflow
    Hyperparameters Optimization and Training a Random Forest
    Machine learning KNIME Data science
    +2
    This workflow optimizes the hyperparameters of a random forest of decision trees and training it with the optimized hyperparamete…
    knime > Examples > 06_Control_Structures > 04_Loops > 21_Parameter_optimization_loop > parameter_optimization_simple
    1
    knime
  15. Workflow
    Advanced Machine Learning - Exercise (Solution)
    Parameter optimization Random forest Machine learning
    +2
    This workflow shows a solution to a hands-on exercise in the L2-DS Introduction to KNIME Analytics Platform for Data Scientists -…
    knime > Education > Self-Paced Courses > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > Solutions > 04 Advanced Machine Learning - Solution
    1
    knime
  16. Node / Learner
    H2O Random Forest Learner (Regression) (deprecated)
    Analytics Integrations H2O Machine Learning
    +2
    Learns a Distributed Random Forest (DRF) regression model, which is a special version of the random forest* algorithm provided by…
    0
    knime
  17. Node / Learner
    H2O Random Forest Learner (deprecated)
    Analytics Integrations H2O Machine Learning
    +2
    Learns a Distributed Random Forest (DRF) classification model, which is a special version of the random forest* algorithm provide…
    0
    knime
  18. Node / Learner
    H2O Random Forest Learner (deprecated)
    Analytics Integrations H2O Machine Learning
    +2
    Learns a Distributed Random Forest (DRF) classification model, which is a special version of the random forest* algorithm provide…
    0
    knime
  19. Node / Learner
    H2O Random Forest Learner
    Analytics Integrations H2O Machine Learning
    +2
    Learns a Distributed Random Forest (DRF) classification model, which is a special version of the random forest* algorithm provide…
    0
    knime
  20. Node / Learner
    H2O Random Forest Learner (Regression)
    Analytics Integrations H2O Machine Learning
    +2
    Learns a Distributed Random Forest (DRF) regression model, which is a special version of the random forest* algorithm provided by…
    0
    knime