Challenge 25 - Detecting the Presence of Heart Disease

0 ×

Level: Medium

Description: You work as a data scientist for a healthcare company attempting to create a predictor for the presence of heart disease in patients. Currently, you are experimenting with 11 different features (potential heart disease indicators) and the XGBoost classification model, and you noticed that its performance can change quite a bit depending on how it is tuned. In this challenge, you will implement hyperparameter tuning to find the best values for XGBoost's Number of Boosting Rounds, Max Tree Depth, and learning rate hyperparameters. Use metric F-Measure as the objective function for tuning.

Nodes

• Column Filter7 ×
• Column Selection Configuration6 ×
• CASE Switch Start5 ×
• Rule Engine Variable5 ×
• Table Row to Variable5 ×
• Show all 49 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Integrated Deployment
• FeatureKNIME JavaScript Views
• FeatureKNIME JavaScript Views (Labs)
• Show all 9 modules

Links

• No links available