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Final Project flow 1

Test DataRemove Rowmin-max 0-1 NormalizerModel PredictorModel LearnerModel PredictorData statisticsNode 30Model LearnerScorerR^2=0.554ScorerScorerR^2=0.555Train dataProduct StoreSales BoxplotProduct Type Bar ChartMax Retail Price per Product SalesHistogramModel LearnerModel PredictorModel LearnerModel PredictorScorerR^2=0.597Best fittedParamerers selectionLoopbest fitted boosting roundsNumber= 100best degree= 3Best fittedParamerers selectionLoopBest fittedParamerers selectionLoopbest fittednumber of Models: 200NumericFeature Selection80-20 PartitioningModel LearnerModel PredictorScorerBest fitted featuresMax R^2 FeaturesLoop End80-20 PartitioningProduct FatProduct Type One to ManyModel PredictorScorerNumericFeature Selection80-20 PartitioningMax R^2 FeaturesLoop EndBest fitted featuresModel Learner80-20 PartitioningNumericFeature Selection80-20 PartitioningMax R^2 FeaturesLoop EndBest fitted features80-20 PartitioningModel LearnerModel PredictorScorerR^2=0.553NumericFeature Selection80-20 PartitioningModel LearnerModel PredictorScorerMax R^2 FeaturesLoop EndBest fitted features80-20 PartitioningStore LocationStore TypeProduct FatStore SizeStore LocationProduct Type One to ManyProduct FatStore Sizemin-max 0-1 NormalizerStore TypeProduct FatMissing values to'Missing' or MediaTest Data predictorDenormalizationNode 137Node 138Node 139Node 140Node 141Node 142Node 143Node 144Node 145Node 146CSV Reader Missing Value Normalizer RegressionPredictor Linear RegressionLearner RegressionPredictor Statistics Linear Correlation PolynomialRegression Learner Numeric Scorer Numeric Scorer Numeric Scorer CSV Reader Box Plot Bar Chart Histogram XGBoost Linear EnsembleLearner (Regression) XGBoost Predictor(Regression) Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End ParameterOptimization Loop End Parameter OptimizationLoop Start Parameter OptimizationLoop Start ParameterOptimization Loop End Feature SelectionLoop Start (1:1) Partitioning XGBoost Linear EnsembleLearner (Regression) XGBoost Predictor(Regression) Numeric Scorer Feature SelectionFilter Feature SelectionLoop End Partitioning Rule Engine One to Many RegressionPredictor Numeric Scorer Feature SelectionLoop Start (1:1) Partitioning Feature SelectionLoop End Feature SelectionFilter PolynomialRegression Learner Partitioning Feature SelectionLoop Start (1:1) Partitioning Feature SelectionLoop End Feature SelectionFilter Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Feature SelectionLoop Start (1:1) Partitioning Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Feature SelectionLoop End Feature SelectionFilter Partitioning Rule Engine Rule Engine Rule Engine Rule Engine Rule Engine One to Many Rule Engine Rule Engine Normalizer Rule Engine Rule Engine Missing Value Random Forest Predictor(Regression) Denormalizer GroupBy Cell Replacer Column AutoType Cast Column Merger Column Filter GroupBy Cell Replacer Column AutoType Cast Column Merger Column Filter Test DataRemove Rowmin-max 0-1 NormalizerModel PredictorModel LearnerModel PredictorData statisticsNode 30Model LearnerScorerR^2=0.554ScorerScorerR^2=0.555Train dataProduct StoreSales BoxplotProduct Type Bar ChartMax Retail Price per Product SalesHistogramModel LearnerModel PredictorModel LearnerModel PredictorScorerR^2=0.597Best fittedParamerers selectionLoopbest fitted boosting roundsNumber= 100best degree= 3Best fittedParamerers selectionLoopBest fittedParamerers selectionLoopbest fittednumber of Models: 200NumericFeature Selection80-20 PartitioningModel LearnerModel PredictorScorerBest fitted featuresMax R^2 FeaturesLoop End80-20 PartitioningProduct FatProduct Type One to ManyModel PredictorScorerNumericFeature Selection80-20 PartitioningMax R^2 FeaturesLoop EndBest fitted featuresModel Learner80-20 PartitioningNumericFeature Selection80-20 PartitioningMax R^2 FeaturesLoop EndBest fitted features80-20 PartitioningModel LearnerModel PredictorScorerR^2=0.553NumericFeature Selection80-20 PartitioningModel LearnerModel PredictorScorerMax R^2 FeaturesLoop EndBest fitted features80-20 PartitioningStore LocationStore TypeProduct FatStore SizeStore LocationProduct Type One to ManyProduct FatStore Sizemin-max 0-1 NormalizerStore TypeProduct FatMissing values to'Missing' or MediaTest Data predictorDenormalizationNode 137Node 138Node 139Node 140Node 141Node 142Node 143Node 144Node 145Node 146CSV Reader Missing Value Normalizer RegressionPredictor Linear RegressionLearner RegressionPredictor Statistics Linear Correlation PolynomialRegression Learner Numeric Scorer Numeric Scorer Numeric Scorer CSV Reader Box Plot Bar Chart Histogram XGBoost Linear EnsembleLearner (Regression) XGBoost Predictor(Regression) Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End ParameterOptimization Loop End Parameter OptimizationLoop Start Parameter OptimizationLoop Start ParameterOptimization Loop End Feature SelectionLoop Start (1:1) Partitioning XGBoost Linear EnsembleLearner (Regression) XGBoost Predictor(Regression) Numeric Scorer Feature SelectionFilter Feature SelectionLoop End Partitioning Rule Engine One to Many RegressionPredictor Numeric Scorer Feature SelectionLoop Start (1:1) Partitioning Feature SelectionLoop End Feature SelectionFilter PolynomialRegression Learner Partitioning Feature SelectionLoop Start (1:1) Partitioning Feature SelectionLoop End Feature SelectionFilter Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Feature SelectionLoop Start (1:1) Partitioning Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Feature SelectionLoop End Feature SelectionFilter Partitioning Rule Engine Rule Engine Rule Engine Rule Engine Rule Engine One to Many Rule Engine Rule Engine Normalizer Rule Engine Rule Engine Missing Value Random Forest Predictor(Regression) Denormalizer GroupBy Cell Replacer Column AutoType Cast Column Merger Column Filter GroupBy Cell Replacer Column AutoType Cast Column Merger Column Filter

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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: Acer
Created at: 2020-01-29
On NodePit since: 2022-02-06
Last update: 2024-11-21
Created with KNIME version: v4.1.0

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