KNIME: The Konstanz information miner

.4 KNIME: The Konstanz information miner

• KNIME’s Machine Learning Capabilities: KNIME offers a range of machine learning models for classification, regression, dimension reduction, and clustering, employing techniques like deep learning, tree-based algorithms, and logistic regression.
• Model Performance Optimization: KNIME allows for model performance improvement through methods like hyperparameter tuning, boosting, bagging, stacking, and ensemble creation. Performance is evaluated using metrics like accuracy, R-squared, AUC, and ROC.
• Model Explainability and Visualization: KNIME utilizes LIME and Shapley values for model explainability. It provides interactive visualizations of partial dependency and ICE plots for enhanced model understanding.
• Data Analysis and Reporting: KNIME offers user-customizable charts for data visualization and facilitates report generation in formats like PDF and PowerPoint. It also allows saving processed data or analysis results in various file formats or databases.
• Scalable Execution: KNIME supports workflow prototyping for testing different data analysis approaches. It enables parallel processing and streaming data for faster workflow execution. KNIME leverages Apache Spark for distributed computing and database processing, enhancing scalability.
• Integration and Extensions: KNIME integrates with Apache Hadoop and other Hadoop data storage systems like Hive and Impala. It allows for modeling and execution of Apache Spark jobs.
• HiLiting System: KNIME’s HiLiting system enables highlighting rows in data tables, with the highlighting being reflected across other views displaying the same data. This system extends to nodes that alter table structure but preserve row relationships, including clustering techniques.