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
Workflow
Group 2 Training, Evaluation and Optimization
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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