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01_​KNIME_​for_​Performance

Performance and Scalability Testing: Example all native KNIME nodes

This workflows show how to learn a random forest using native KNIME nodes.

We here are measuring the speed of the workflow with the last metanode. In addition it collects the max used memory and the start parameters of this instantiation of the KNIME Analytics Platform.

Preprocessing Performance and Scalability Testing: Example all native KNIME nodesThis workflows show how to learn a random forest using native KNIME nodes. Data Conversion and Transfer Read Input DataPredictFamily StateReceivedata from caller workfowIncrease iterationsfor mutliple evaluationrounds Combine File Reader Partitioning Random ForestLearner Random ForestPredictor Scorer Missing Value CSV Writer ContainerOutput (Table) ContainerInput (Table) Capture Accuracy Benchmark End(Memory Monitoring) Benchmark Start(Memory Monitoring) Preprocessing Performance and Scalability Testing: Example all native KNIME nodesThis workflows show how to learn a random forest using native KNIME nodes. Data Conversion and Transfer Read Input DataPredictFamily StateReceivedata from caller workfowIncrease iterationsfor mutliple evaluationroundsCombine File Reader Partitioning Random ForestLearner Random ForestPredictor Scorer Missing Value CSV Writer ContainerOutput (Table) ContainerInput (Table) Capture Accuracy Benchmark End(Memory Monitoring) Benchmark Start(Memory Monitoring)

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: iris.adae
Created at: 2019-02-15
On NodePit since: 2019-07-12
Last update: 2024-04-18
Created with KNIME version: v4.1.1

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