KNIME Verified Components

Easily reuse bundled functionalities, verified by KNIME experts.

A set of 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:

Parameter Optimization (Table)

This component performs a (hyper)parameter optimization for any classification model provided at its input given a few settings such as cross validation. To get hands-on examples on the component and the nodes inside check the Parameter Optimization for Classification space.

Category: Automation

Author: Keerthan Shetty, Data Science Intern at KNIME and Paolo Tamagnini, Data Scientist at KNIME

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Icon for Parameter Optimization (Table)

Browse through our previous verified components.

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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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 2021 we’d like to highlight and recommend three Components shared by Ashok K Harnal, Professor at FORE School of Management in New Delhi - our Contributor of the Month for November 2021.



This community component embeds a Python Script and a Conda Environment to automatically encode with 4 different methods categorical columns from string to double type. Optionally you can create an embedding of N dimensions via PCA where N is optimized in order to explain at least 95% of the original variance. The component requires both train and test sets to properly output transformations.

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This community component automatically installs and executes the ‘autofeat’ Python package via a Conda Environment Propagation and Python Script nodes. You can apply this component before training a model to automatically perform feature engineering on numerical data: creating new columns by applying mathematical transformation on existing ones.

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If you want to deploy or validate a model trained on data from the Autofeat Generator, you have to use this other community component first to generate the same engineered features from a new partition of data.

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

KNIME Components Drag and Drop

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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Browse all Components

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

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About KNIME Hub

Learn more about the KNIME Hub and how it can help with 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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