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regression_​test_​feature_​selection

Pre-processing - Convert weather data type to string - Join product data and weather data - Reserve 80% of the rows for model training and remaining for model testing - Use same number of data rows for both classes in testing Score the ModelRemember to use the Predictornode appropriate for your model!Evaluate predictions based onconfusion matrix and ROC. Train a ModelTree Learner for training Data Reading Two files: Product "Boulette -Imbiss, Pack= 30 St.170g" +Weather data Reserve 80% of the rows for model training and remaining for model testingJoin product data and weather dataAdjust data typeAdjust data typeProduct dataWeather dataFiltering columswithout useLearning on Menge kgTesting knowledge onMenge kgResultspredictionvs.original dataAdjust datatype"Schneeböhe"column includes "-", in need fora string replacerto convert themto 0Resultspredictionvs.original dataLearning on Menge kgTesting knowledge onMenge kg"Datum" toDate&TimetypeNode 136Sort by"Datum" Reserve 80% of the rows for model training and remaining for model testingTesting knowledge onMenge kgLearning on Menge kgResultspredictionvs.original data Reserve 80% of the rows for model training and remaining for model testing Forward FeatureSelection Partitioning Joiner Date&Time to String Date&Time to String Excel Reader Excel Reader Column Filter Simple RegressionTree Learner Simple RegressionTree Predictor Numeric Scorer String To Number String Replacer Numeric Scorer Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) String to Date&Time Lag Column Sorter Partitioning Gradient Boosted TreesPredictor (Regression) Gradient Boosted TreesLearner (Regression) Numeric Scorer Partitioning Pre-processing - Convert weather data type to string - Join product data and weather data - Reserve 80% of the rows for model training and remaining for model testing - Use same number of data rows for both classes in testing Score the ModelRemember to use the Predictornode appropriate for your model!Evaluate predictions based onconfusion matrix and ROC. Train a ModelTree Learner for training Data Reading Two files: Product "Boulette -Imbiss, Pack= 30 St.170g" +Weather data Reserve 80% of the rows for model training and remaining for model testingJoin product data and weather dataAdjust data typeAdjust data typeProduct dataWeather dataFiltering columswithout useLearning on Menge kgTesting knowledge onMenge kgResultspredictionvs.original dataAdjust datatype"Schneeböhe"column includes "-", in need fora string replacerto convert themto 0Resultspredictionvs.original dataLearning on Menge kgTesting knowledge onMenge kg"Datum" toDate&TimetypeNode 136Sort by"Datum" Reserve 80% of the rows for model training and remaining for model testingTesting knowledge onMenge kgLearning on Menge kgResultspredictionvs.original data Reserve 80% of the rows for model training and remaining for model testing Forward FeatureSelection Partitioning Joiner Date&Time to String Date&Time to String Excel Reader Excel Reader Column Filter Simple RegressionTree Learner Simple RegressionTree Predictor Numeric Scorer String To Number String Replacer Numeric Scorer Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) String to Date&Time Lag Column Sorter Partitioning Gradient Boosted TreesPredictor (Regression) Gradient Boosted TreesLearner (Regression) Numeric Scorer Partitioning

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Created by: Jannis
Created at: 2022-01-03
On NodePit since: 2022-02-06
Last update: 2024-11-24
Created with KNIME version: v4.5.0

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