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Group 2 Training, Evaluation and Optimization

Predictive Analytics Machine Learning Parameter Optimization Scoring ROC
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Solution to the tasks for Group 2 in KNIME Data Science Learnathon - Train a Decision Tree on the training set, and apply the model to the test set - Evaluate the performance of the Decision Tree model - Train a Logistic Regression model on the training set, and apply the model to the test set - Evaluate the performance of the Logistic Regression model - Optimize the tree depth of a Random Forest model, and train and apply a Random Forest model using the optimal parameter value - Evaluate the performance of the Random Forest model - Compare the performances of the different models using scoring metrics for a classification model and an ROC Curve - Write the best performing model to a file

External resources

  • KNIME Analytics: a Review
  • Building a Basic Model for Churn Prediction with KNIME
  • Model Selection and Management with KNIME
  • Behind the Scenes of Decision Tree with KNIME
  • Decision Tree Learner Node: Algorithm Settings
  • Ensemble Learning
  • Import Existing Models
  • KNIME E-Learning Course - Predictive Analytics
  • From Modeling to Scoring: Confusion Matrix and Class Statistics
  • Scoring Metrics for Classification Models
  • Cross Validation with SVM
  • Cross-validation (statistics)
  • Parameter Optimization for Prediction Models
  • Analytics - Model Selection to Predict Flight Departure Delays
  • Original Airline Dataset

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.2
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

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    KNIME Excel Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

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