JKISeason3-18
This is an example for computing explanation using LIME. AutoML component was used to pick the best model, but any model and its set of Learner and […]
KNIME_challenge24_solution
KNIME_challenge23_solution Challenge 24: Modeling Churn Predictions - Part 2 Level: Easy to Medium Description: Just like in last week’s […]
Manage List of Executor Images
URL: Admin Workflows https://hub.knime.com/knime/collections/Admin%20resources%20for%20KNIME%20Business%20Hub~uGcJRmfZfqTmEfub
AutoML Regression and Classification Examples
URL: KNIME Integrated Deployment - KNIME.com https://www.knime.com/integrated-deployment URL: Guided Automation: a more complex AutoML example with KNIME […]
01_Model_Monitoring_with_AutoML_and_MLOps
Model Monitoring with Integrated Deployment This is an example showing how you can monitor a deployed model using Integrated Deployment and Guided […]
01_Model_Monitoring_with_Integrated_Deployment
This an example showing how you can monitor a deployed model using Integrated Deployment and Guided Analytics.
01_Analytics
Analytics - Model Selection to Predict Flight Departure Delays This workflow trains a number of data analytics models and automatically selects the best […]
JKISeason2-9
Uses Linear Regression to produce signifiance factor coefficients. Uses AutoMl to produce best model (Gradient Boosted Trees.)
Install CDDS using a KNIME data app (Hub v1.5 to v1.12)
1. Execute this component and open its interactive View. 2. Configure Hub connections and CDDS in the interactive View. 3. Close the View.