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Group2_​1_​Training_​Evaluation_​and_​Optimization

Group 2 Training, Evaluation and Optimization
Bag of Models Challenge: Model Training, Evaluation and OptimizationGoal: 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 contains 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 the GHCN_daily_readme file in the “data” folder)Suggested Steps: Group 2. Model Training to Predict Departure Delays training set test set Bag of Models Optional See instructions inside the metanodeOpen by- double-clickOR- Right-click ->Metanode -> OpenExecute this metanodefirst! First ML Model -Decision Tree Second ML Model -Logistic Regression Third ML Model - Gradient BoostedTree with Parameter Optimization Data Access, Cleaning,and Partitioning Bag of Models Challenge: Model Training, Evaluation and OptimizationGoal: 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 contains 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 the GHCN_daily_readme file in the “data” folder)Suggested Steps: Group 2. Model Training to Predict Departure Delays training set test set Bag of Models Optional See instructions inside the metanodeOpen by- double-clickOR- Right-click ->Metanode -> OpenExecute this metanodefirst! First ML Model -Decision Tree Second ML Model -Logistic Regression Third ML Model - Gradient BoostedTree with Parameter Optimization Data Access, Cleaning,and Partitioning

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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: v4.3.2
Tags: Predictive AnalyticsMachine LearningParameter OptimizationScoringROCBag of ModelsDecision TreeLogistic RegressionRandom Forest

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