KNIME Verified Components

Easily reuse bundled functionalities, verified by KNIME experts.

A set of trustworthy Components that behave like KNIME nodes, including error handling capabilities, which are developed by KNIME and regularly released on the KNIME Hub. We'll regularly update this page with new Components - here are the most recent ones:

XAI View

This component for eXplainable AI (XAI) can explain predictions of a black-box model created with the AutoML component via SHAP, partial dependence and a surrogate decision tree from KNIME MLI Extension. The generated composite view is interactive and can be customized by adding JavaScript or Plotly View nodes.

Category: Model Interpretability

Authors: Mahantesh Pattadkal, Data Science Intern at KNIME and Paolo Tamagnini, Data Scientist at KNIME

 

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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.

Category: Automation

Author: Temesgen H. Dadi, Technical Data Scientist at KNIME

View 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.

Category: Automation

Author: Janina Mothes, Life Science Data Scientist at KNIME

View 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

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.

View on KNIME Hub

Data Manipulation

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.

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Finance Analytics

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.

View on KNIME Hub

knime_icons_rz Guided Analytics

Components can generate views when using Widget and JavaScript nodes. Those views can be used on their own or in sequence to interactively guide the user through the analysys. Use the Guided Analytics Components to create guided analytics workflows for local usage or remote access via KNIME WebPortal.

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Life Sciences

Gather and analyse Life Science data with shared Components. For example, extract data from the European Nucleotide Archive, ChEMBL or PDB or perform a Pathway Enrichment Analysis by simply dragging and dropping the Component of your choice.

View on KNIME Hub

knime_icons_rz Model Interpretability

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.

View on KNIME Hub

Text Processing

The text processing Components help you to analyse text documents - from extracting data from biomedical literature, to document preprocessing, through to computing document similarity.

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knime_icons_rz Time Series

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.

View on KNIME Hub

Visualizations

Countless visualizations exist to display data in colorful charts. Most of those visualizations are already provided in KNIME via JavaScript and Plotly nodes. If you cannot find the plot you need as a standard node, make sure to check this category of Components with more complex visualizations or interactive composite views.

 

View on KNIME Hub

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!

KNIME Component GUID Generator
A Globally Unique Identifier (GUID) generator for KNIME. This component is useful for creating a unique key that is not based on Row ID.
KNIME Component Text Preprocessing
This component uses extremely fast regex-based text processing to remove specific types of characters from a String column and normalize the data as much as possible, without over-processing.

Build your own Components and share them with the KNIME Community on the KNIME Hub!

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.

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knime_icons_rz Browse all components

Visit the KNIME Hub to browse all available components and add them to your workflow.

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knime_icons_rz About KNIME Hub

Learn more about the KNIME Hub and find your data science solutions.

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Read the blog

Learn more about the difference between components and metanodes in KNIME.

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