KNIME Verified Components
Easily reuse bundled functionalities, verified by KNIME experts.
Category: Model Interpretability
View Component View example workflow
Get Versions of Nodes in Workflow
This component can be added to a workflow to get a unique list of nodes used in the workflow and the version number of a KNIME feature containing those nodes. The results can be included in reports and data exports for documentation purposes.
Author: Temesgen H. Dadi, Technical Data Scientist at KNIMEView Component View example workflow
U-Net 2D - Encoding/Decoding Layers
These two components can be used to create a U-Net for 2D data with the Keras Deep Learning Integration. This kind of deep learning architecture was originally created for biomedical image segmentation, but is now frequently used for general image segmentation. On KNIME Hub, both Encoding and Decoding Components are available.
Author: Janina Mothes, Life Science Data Scientist at KNIMEView Encoding Component View example workflow
Verified Component Categories
Verified components are officially released by the KNIME team and are divided into the following categories. Explore all the verified Components in each category on the KNIME Hub.
Automation Components help when a workflow has to be executed in a production environment in an automated fashion - from complex AutoML to simple tricks to increase flexibility and traceability of your workflow.
When dealing with raw data, there are recurrent data manipulation techniques requiring complex workflows. We offer those workflows via Components to quickly clean, rename, filter, and transform raw data columns.
Finance teams across all industries require specific techniques to keep track of the company’s numbers while ensuring precision and compliance. Those time-consuming tasks usually rely on data from different spreadsheets, which can be hard to maintain and update. These Components help build reusable workflows for an efficient financial analysis.
Training performant predictive models often leads to black boxes: data goes in the box, it's processed by nearly incomprehensible algorithms, predictions come out of the box. KNIME offers an extension to be combined with these Components for Machine Learning Interpretability (MLI) and Explainable AI (XAI) use cases.
The text processing Components help you to analyse text documents - from extracting data from biomedical literature, to document preprocessing, through to computing document similarity.
Easily clean, aggregate, visualize, and forecast time series data with this set of Components. Includes options for seasonality visualization and removal, ACF and PACF plots, as well as ARIMA forecasting.
Community Component Highlights:
Components can also be shared by community members via the KNIME Hub. In this section, we promote the Community Components that have been downloaded the most. These are selected and reviewed by KNIME experts and each quarter shared in this section.
For Q4 2020 we’d like to highlight and recommend two Components shared by SJ Porter - our Contributor of the Month for October!
What are Components?
Components are really KNIME nodes that you create with a KNIME workflow, enabling you to easily bundle, reuse, and share functionality. Configuration and widget nodes allow you to create Components that behave just like normal nodes with a logo, a dialogue, and often interactive views. With KNIME Analytics Platform, anyone can create Components and share them, via the KNIME Hub, with the community.Learn more
Browse all components
Visit the KNIME Hub to browse all available components and add them to your workflow.
About KNIME Hub
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Read the blog
Learn more about the difference between components and metanodes in KNIME.