Icon

DiamondData

Diamond Data
1. Reading the data file 2. Visual explorationof the data 3. Missing value handling 4. Partitioning 6. Building the first model(Equation) 7. Predicting the targetwith the first model(Estimation) 8. Calculating thegoodness-of-fit metricsfor the first model(Evaluation) 9. Building the secondmodel (Equation) 10. Predicting the targetwith the second model(Estimation) 11. Calculating thegoodness-of-fit metricsfor the second model(Evaluation) 5. Correlationfiltering Reading thediamond dataVisualize thedataConvert 0.0 tomissing valueRemoverows withmissing valuesTraining: 70%Testing: 30%Training alinear regressionmodelPrediction basedon the trained modelGoodness of fitmetricsDropping featureswith r > 0.80Node 13Prediction basedon the trained modelGoodness of fitmetrics CSV Reader Data Explorer Math Formula(Multi Column) Missing Value Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer CorrelationFiltering Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Numeric Scorer 1. Reading the data file 2. Visual explorationof the data 3. Missing value handling 4. Partitioning 6. Building the first model(Equation) 7. Predicting the targetwith the first model(Estimation) 8. Calculating thegoodness-of-fit metricsfor the first model(Evaluation) 9. Building the secondmodel (Equation) 10. Predicting the targetwith the second model(Estimation) 11. Calculating thegoodness-of-fit metricsfor the second model(Evaluation) 5. Correlationfiltering Reading thediamond dataVisualize thedataConvert 0.0 tomissing valueRemoverows withmissing valuesTraining: 70%Testing: 30%Training alinear regressionmodelPrediction basedon the trained modelGoodness of fitmetricsDropping featureswith r > 0.80Node 13Prediction basedon the trained modelGoodness of fitmetricsCSV Reader Data Explorer Math Formula(Multi Column) Missing Value Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer CorrelationFiltering Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Numeric Scorer

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: Satoru
Created at: 2021-02-10
On NodePit since: 2021-11-15
Last update: 2024-04-26
Created with KNIME version: v4.3.1
Tags: correlation filteringlinear regressiongradient boosted tree

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!