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:


This Component automatically trains supervised machine learning models for both binary and multiclass classification. The Component is able to automate the whole ML cycle by performing some data preparation, parameter optimization with cross validation, scoring, evaluation and selection. It also captures the entire end to end process and outputs the deployment workflow using the KNIME Integrated Deployment Extension.

Category: Automation

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


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Analyze Workflow Summary

This Component extracts information from a workflow summary XML file that can be obtained from either the File > Export > Workflow Summary (JSON/XML)… menu in KNIME Analytics Platform, or from a KNIME Server’s REST API. You can use this Component to easily retrieve metadata of a KNIME Workflow for data lineage and governance purposes.

Category: Automation

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

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SHAP Summarizer

This Component can be used before the bottom input port of SHAP Loop Start. This technique uses k-means to summarize the validation set and create a sampling table to use when creating coalitions. The created sampling table is large n rows, each row is a different prototype of the data. This n can be adjusted from the configuration dialogue of the Component. The n default value is 100. The output sampling table has, for each of the n clusters created by k-means, a prototype row and a column "SHAP Summarizer Sampling weight" that can be used by the SHAP Loop Start node.

Category: Model Interpretability

Author: Mahantesh Pattadkal, Data Science Intern at KNIME

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Molecular Properties Filter

This Component allows you to select a subset of molecules based on the input data and molecular properties calculated using the RDKit Descriptor Calculation node. The Interactive View depicts the molecular properties in a parallel coordinates plot, and the molecules' structural formula as tiles. The selected molecules are provided in the Component output. Note that columns from the input table is by default propagated to the parallel coordinates plot.

Category: Life Sciences

Author: Alice Krebs, Life Science Data Scientist at KNIME

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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.

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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.

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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.

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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.

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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.

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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.

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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 Build a component

Use the KNIME Documentation to build your own component and share it 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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Read the blog

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

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