Automatically trains supervised machine learning models for a regression task. It's 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 a workflow port object using the KNIME Integrated Deployment Extension. To automatically train models for a classification task, check instead its twin version: the AutoML component.
Category: AutomationView Component View Workflow
This component uses a heuristic approach to analyze the target series and fit a (S)ARIMA model for forecasting with automatically configured hyper-parameters. Auto-SARIMA stands for Automated Seasonal Autoregressive Integrated Moving Average: an automated training of a SARIMA model.
Category: Time Series
Author: Corey Weisinger, Data Scientist at KNIMEView Component View 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 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.
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.
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
Learn more about the KNIME Hub and how it can help with your data science solutions.
Read the Blog
Learn more about the difference between components and metanodes in KNIME.