// Workflow steps:
// 1. File Reader -> load dataset
// 2. Cross Validation Loop Start
// 3. Decision Tree Learner -> train model inside loop
// 4. Predictor -> predict inside loop
// 5. Scorer -> evaluate inside loop
// 6. Cross Validation Loop End -> collect resultsCoding works best on desktop or with an external keyboard.
Using cross-validation to evaluate classifier performance in KNIME.
// Workflow steps:
// 1. File Reader -> load dataset
// 2. Cross Validation Loop Start
// 3. Decision Tree Learner -> train model inside loop
// 4. Predictor -> predict inside loop
// 5. Scorer -> evaluate inside loop
// 6. Cross Validation Loop End -> collect resultsKNIME (Konstanz Information Miner) is an open-source, modular, and visual data analytics platform that enables users to create end-to-end data pipelines, including data preprocessing, analytics, machine learning, and reporting, using a drag-and-drop workflow interface.
Origin & Creator
KNIME was developed at the University of Konstanz, Germany, starting in 2004, to support data mining research and practical workflow creation for analytics.
Industrial Note
KNIME is widely used in research, life sciences, finance, marketing, and industrial analytics where reproducible, end-to-end workflows are required, especially when combining multiple data sources and technologies.