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3. decision tree classification

Data ReadingRead the new file 'newpro.csv'created after data preprocessing.It contains:1. Trap Type2. Number of Mosquitoes3. Species4. Test Results Data PartitioningCreate two separate partitions ondata set1. training set (80%) 2. test set (20%). Train a ModelThis node builds a decision tree.Most Learner nodes output a PMMLmodel (blue square output port). Apply the ModelPredictor nodes apply a specificmodel to a data set and append themodel predictions. Score the ModelCompute a confusion matrixbetween real and predicted classvalues and calculate the relatedaccuracy measures. ROC Curve Evaluate predictions on basis ofROC Curve training set test set Simple Model Training for Decision Tree ClassificationThis workflow demonstrates how a simple decision tree classifier is built andapplied to new data. It also illustrates the use of KNIME's hiliting capabilities,which allow interactive views to be connected within the same workflow.Task Predict the whether the species has virus or not from demographicattributes of the west nile virus data set. Reading westnilevirus.csvRandom drawing 80% upper port20% lower portTarget Class = ResultApply the trained modelto predict ResultsMatch original vs. predicted Result valuesBuild ROC for predictions File Reader Partitioning DecisionTree Learner Decision TreePredictor Scorer ROC Curve (local) Data ReadingRead the new file 'newpro.csv'created after data preprocessing.It contains:1. Trap Type2. Number of Mosquitoes3. Species4. Test Results Data PartitioningCreate two separate partitions ondata set1. training set (80%) 2. test set (20%). Train a ModelThis node builds a decision tree.Most Learner nodes output a PMMLmodel (blue square output port). Apply the ModelPredictor nodes apply a specificmodel to a data set and append themodel predictions. Score the ModelCompute a confusion matrixbetween real and predicted classvalues and calculate the relatedaccuracy measures. ROC Curve Evaluate predictions on basis ofROC Curve training set test set Simple Model Training for Decision Tree ClassificationThis workflow demonstrates how a simple decision tree classifier is built andapplied to new data. It also illustrates the use of KNIME's hiliting capabilities,which allow interactive views to be connected within the same workflow.Task Predict the whether the species has virus or not from demographicattributes of the west nile virus data set. Reading westnilevirus.csvRandom drawing 80% upper port20% lower portTarget Class = ResultApply the trained modelto predict ResultsMatch original vs. predicted Result valuesBuild ROC for predictions File Reader Partitioning DecisionTree Learner Decision TreePredictor Scorer ROC Curve (local)

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Created by: Admin
Created at: 2020-04-30
On NodePit since: 2020-05-07
Last update: 2024-04-20
Created with KNIME version: v4.1.2

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