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Group2_​2_​OPTIONAL_​Steps

Group 2 Optional Steps
Challenge: Cross Validation, External Tools and Interval LoopGoal: Train a number of data analytics models to predict departure delays at a selected airport (ORD).Datasets: 1. AirlineDataset.table 2. GHCN-Daily_source.xls containing daily weather information like precipitation, snowfall, snow depth, temperature, wind speed and wind directionmeasured at Chicago O'Hare International Airport. (The explanation of the columns is available in data/GHCN_daily_readme.txt)Suggested Steps: Group 2. Model Training to Predict Departure Delays Execute this metanode first! top: whole datasetmiddle: training setbottom: test setSee instructions inside the metanodeOpen by- double-clickOR- Right-click ->Metanode -> Open Data Access, Preprocessing,and Partitioning Applying Cross Validationwith H2O Integration Interval Loop Challenge: Cross Validation, External Tools and Interval LoopGoal: Train a number of data analytics models to predict departure delays at a selected airport (ORD).Datasets: 1. AirlineDataset.table 2. GHCN-Daily_source.xls containing daily weather information like precipitation, snowfall, snow depth, temperature, wind speed and wind directionmeasured at Chicago O'Hare International Airport. (The explanation of the columns is available in data/GHCN_daily_readme.txt)Suggested Steps: Group 2. Model Training to Predict Departure Delays Execute this metanode first! top: whole datasetmiddle: training setbottom: test setSee instructions inside the metanodeOpen by- double-clickOR- Right-click ->Metanode -> Open Data Access, Preprocessing,and Partitioning Applying Cross Validationwith H2O Integration Interval Loop

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Created by: Maarit Laukkanen
Created at: 2018-09-11
On NodePit since: 2021-04-15
Last update: 2024-04-28
Created with KNIME version: v4.3.1
Tags: Predictive AnalyticsMachine LearningCross ValidationScoringBag of ModelsModels via external integrationH2Ointerval loopgradient boosting

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