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  "Title": "Multi-Label Prediction Using Gibbs Sampling (and Classifier\nChains)",
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  "Date": "2016-07-05",
  "Authors@R": "person(\"Mikhail\", \"Popov\", email = \"mikhail@mpopov.com\",\nrole = c(\"aut\", \"cre\"), comment = \"@bearloga on Twitter\")",
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  "Description": "An implementation of classifier chains (CC's) for\nmulti-label prediction. Users can employ an external package\n(e.g. 'randomForest', 'C50'), or supply their own. The package\ncan train a single set of CC's or train an ensemble of CC's --\nin parallel if running in a multi-core environment. New\nobservations are classified using a Gibbs sampler since each\nunobserved label is conditioned on the others. The package\nincludes methods for evaluating the predictions for accuracy\nand aggregating across iterations and models to produce binary\nor probabilistic classifications.",
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  "BugReports": "https://github.com/bearloga/MLPUGS/issues",
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  "Repository": "https://bearloga.r-universe.dev",
  "Date/Publication": "2020-03-06 17:45:11 UTC",
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      "page": "MLPUGS-package",
      "title": "MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)",
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        "MLPUGS"
      ]
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      "title": "Fit an Ensemble of Classifier Chains (ECC)",
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        "ecc"
      ]
    },
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      "title": "FiveThirtyEight's Movie Scores",
      "topics": [
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        "movies_test",
        "movies_train"
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    {
      "page": "predict.ECC",
      "title": "Classify new samples using an Ensemble of Classifier Chains",
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      ]
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        "Note on Parallelization",
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        "Training an Ensemble of Classifier Chains (ECC)",
        "Prediction Using Gibbs Sampling (PUGS)",
        "Gathering Predictions",
        "Evaluation",
        "Further Reading"
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