PredictPackage2.0

PredictPackage2.0

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Contributor(s)

Initial contribute: 2020-12-20

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{{htmlJSON.Style}} SimpleCalculation
{{htmlJSON.Categories}} Machine learning / RandomForest
{{htmlJSON.Language}} EN_US
{{htmlJSON.Name}} IMGRandomForest_Predicting
{{htmlJSON.Keywords}} Image
{{htmlJSON.Abstract}} Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.[1][2] Random decision forests correct for decision trees' habit of overfitting to their training set.
{{htmlJSON.Wiki}} https://en.wikipedia.org/wiki/Random_forest
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{{htmlJSON.Type}} {{htmlJSON.Property}} {{htmlJSON.Value}}
{{htmlJSON.HardwareConfigures}} Main Frequency 1.0
Memory Size 1024M
{{htmlJSON.Assemblies}} python.exe $(DataMappingPath)\Python27\

{{htmlJSON.HowtoCite}}

FengyuanZhang (2020). PredictPackage2.0, Computable Model, OpenGMS, https://geomodeling.njnu.edu.cn/computableModel/6a508392-9741-4282-8473-7f4eb055d493
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Contributor(s)

Initial contribute : 2020-12-20

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