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Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data

Selçuk Demir, Emrehan Kutluğ Şahin

2021Soil Dynamics and Earthquake Engineering75 citationsDOI

Topics & Concepts

Random forestSimple random sampleStratified samplingWilcoxon signed-rank testSampling (signal processing)StatisticsPearson product-moment correlation coefficientComputer scienceCorrelation coefficientMathematicsCone penetration testArtificial intelligenceMachine learningData miningAlgorithmEngineeringDemographyFilter (signal processing)Mann–Whitney U testPopulationSociologyComputer visionGeotechnical engineeringGeotechnical Engineering and Soil MechanicsGeotechnical Engineering and Underground StructuresGeotechnical Engineering and Analysis
Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data | Litcius