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Biomass Analysis & Predictive Modeling for the Bioenergy Sector

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Model tuning Training and prediction Remaining missingvalues ​​are replacedwith the column median A high number ofvalidations providesa stable estimate ofthe MAE An independentpartition is created fromthe train data forperformance evaluation Find hyperparametersthat provide the bestMAE Remaining missing values ​​are replaced with thecolumn median Training is set with the best hyperparameterscalculated with tuning Train readerValidation predictionsPerformance metrics (validation)Test predictionsPerformance metrics (test)Tuning loop startTest readerSave predictionsModel trainingCross validation endEnd tuning loopTrain readerModel trainingTest predictionM.V. imputation(train)M.V. imputation(test)Cross validation startM.V. imputation(train)M.V. imputation(validation)Train (85%)Test (15%)Train preprocessing(missing values)M.V. imputation(test)Train preprocessingTest preprocessingPredicted valuesextractionData visualizationSave trained modelCSV Reader Gradient Boosted TreesPredictor (Regression) Numeric Scorer Gradient Boosted TreesPredictor (Regression) Numeric Scorer Parameter OptimizationLoop Start CSV Reader CSV Writer Gradient Boosted TreesLearner (Regression) X-Aggregator ParameterOptimization Loop End CSV Reader Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Missing Value Missing Value(Apply) X-Partitioner Missing Value Missing Value(Apply) Partitioning Preprocessing Missing Value(Apply) Preprocessing Preprocessing Postprocessing Scatter Plot Statistics Model Writer Model tuning Training and prediction Remaining missingvalues ​​are replacedwith the column median A high number ofvalidations providesa stable estimate ofthe MAE An independentpartition is created fromthe train data forperformance evaluation Find hyperparametersthat provide the bestMAE Remaining missing values ​​are replaced with thecolumn median Training is set with the best hyperparameterscalculated with tuning Train readerValidation predictionsPerformance metrics (validation)Test predictionsPerformance metrics (test)Tuning loop startTest readerSave predictionsModel trainingCross validation endEnd tuning loopTrain readerModel trainingTest predictionM.V. imputation(train)M.V. imputation(test)Cross validation startM.V. imputation(train)M.V. imputation(validation)Train (85%)Test (15%)Train preprocessing(missing values)M.V. imputation(test)Train preprocessingTest preprocessingPredicted valuesextractionData visualizationSave trained modelCSV Reader Gradient Boosted TreesPredictor (Regression) Numeric Scorer Gradient Boosted TreesPredictor (Regression) Numeric Scorer Parameter OptimizationLoop Start CSV Reader CSV Writer Gradient Boosted TreesLearner (Regression) X-Aggregator ParameterOptimization Loop End CSV Reader Gradient Boosted TreesLearner (Regression) Gradient Boosted TreesPredictor (Regression) Missing Value Missing Value(Apply) X-Partitioner Missing Value Missing Value(Apply) Partitioning Preprocessing Missing Value(Apply) Preprocessing Preprocessing Postprocessing Scatter Plot Statistics Model Writer

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