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    "cal_validate_none",
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    "which_equivocal"
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        "tbl",
        "data.frame"
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        ".pred",
        "id"
      ],
      "rows": 2000,
      "table": true,
      "tojson": true
    },
    {
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      "title": "Boosted regression trees predictions",
      "object": "boosting_predictions",
      "class": [
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        "tbl",
        "data.frame"
      ],
      "fields": [
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        ".pred"
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      "rows": 500,
      "table": true,
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      "class": [
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        "tbl",
        "data.frame"
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        ".pred_good",
        "Class"
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      "table": true,
      "tojson": true
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    {
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      "object": "species_probs",
      "class": [
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        "tbl",
        "data.frame"
      ],
      "fields": [
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        ".pred_bobcat",
        ".pred_coyote",
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      "rows": 110,
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      "page": "append_class_pred",
      "title": "Add a 'class_pred' column",
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    },
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      "page": "as_class_pred",
      "title": "Coerce to a 'class_pred' object",
      "topics": [
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    {
      "page": "boosting_predictions",
      "title": "Boosted regression trees predictions",
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        "boosting_predictions_oob",
        "boosting_predictions_test"
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    {
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      "title": "Truncate a numeric prediction column",
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    {
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      "title": "Applies a calibration to a set of existing predictions",
      "topics": [
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        "cal_apply.cal_object",
        "cal_apply.data.frame",
        "cal_apply.tune_results"
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    },
    {
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      "topics": [
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        "cal_estimate_beta.data.frame",
        "cal_estimate_beta.grouped_df",
        "cal_estimate_beta.tune_results"
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    },
    {
      "page": "cal_estimate_isotonic",
      "title": "Uses an Isotonic regression model to calibrate model predictions.",
      "topics": [
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        "cal_estimate_isotonic.data.frame",
        "cal_estimate_isotonic.grouped_df",
        "cal_estimate_isotonic.tune_results"
      ]
    },
    {
      "page": "cal_estimate_isotonic_boot",
      "title": "Uses a bootstrapped Isotonic regression model to calibrate probabilities",
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        "cal_estimate_isotonic_boot.data.frame",
        "cal_estimate_isotonic_boot.grouped_df",
        "cal_estimate_isotonic_boot.tune_results"
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    },
    {
      "page": "cal_estimate_linear",
      "title": "Uses a linear regression model to calibrate numeric predictions",
      "topics": [
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        "cal_estimate_linear.data.frame",
        "cal_estimate_linear.grouped_df",
        "cal_estimate_linear.tune_results"
      ]
    },
    {
      "page": "cal_estimate_logistic",
      "title": "Uses a logistic regression model to calibrate probabilities",
      "topics": [
        "cal_estimate_logistic",
        "cal_estimate_logistic.data.frame",
        "cal_estimate_logistic.grouped_df",
        "cal_estimate_logistic.tune_results"
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    },
    {
      "page": "cal_estimate_multinomial",
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      "topics": [
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        "cal_estimate_multinomial.data.frame",
        "cal_estimate_multinomial.grouped_df",
        "cal_estimate_multinomial.tune_results"
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    },
    {
      "page": "cal_estimate_none",
      "title": "Do not calibrate model predictions.",
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        "cal_estimate_none.data.frame",
        "cal_estimate_none.grouped_df",
        "cal_estimate_none.tune_results"
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    },
    {
      "page": "cal_plot_breaks",
      "title": "Probability calibration plots via binning",
      "topics": [
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        "cal_plot_breaks.data.frame",
        "cal_plot_breaks.grouped_df",
        "cal_plot_breaks.tune_results"
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    },
    {
      "page": "cal_plot_logistic",
      "title": "Probability calibration plots via logistic regression",
      "topics": [
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        "cal_plot_logistic.grouped_df",
        "cal_plot_logistic.tune_results"
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    },
    {
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      "title": "Regression calibration plots",
      "topics": [
        "cal_plot_regression",
        "cal_plot_regression.data.frame",
        "cal_plot_regression.grouped_df",
        "cal_plot_regression.tune_results"
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    },
    {
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      "title": "Probability calibration plots via moving windows",
      "topics": [
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        "cal_plot_windowed.data.frame",
        "cal_plot_windowed.grouped_df",
        "cal_plot_windowed.tune_results"
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    },
    {
      "page": "cal_validate_beta",
      "title": "Measure performance with and without using Beta calibration",
      "topics": [
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        "cal_validate_beta.resample_results",
        "cal_validate_beta.rset",
        "cal_validate_beta.tune_results"
      ]
    },
    {
      "page": "cal_validate_isotonic",
      "title": "Measure performance with and without using isotonic regression calibration",
      "topics": [
        "cal_validate_isotonic",
        "cal_validate_isotonic.resample_results",
        "cal_validate_isotonic.rset",
        "cal_validate_isotonic.tune_results"
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    },
    {
      "page": "cal_validate_isotonic_boot",
      "title": "Measure performance with and without using bagged isotonic regression calibration",
      "topics": [
        "cal_validate_isotonic_boot",
        "cal_validate_isotonic_boot.resample_results",
        "cal_validate_isotonic_boot.rset",
        "cal_validate_isotonic_boot.tune_results"
      ]
    },
    {
      "page": "cal_validate_linear",
      "title": "Measure performance with and without using linear regression calibration",
      "topics": [
        "cal_validate_linear",
        "cal_validate_linear.resample_results",
        "cal_validate_linear.rset"
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    },
    {
      "page": "cal_validate_logistic",
      "title": "Measure performance with and without using logistic calibration",
      "topics": [
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        "cal_validate_logistic.resample_results",
        "cal_validate_logistic.rset",
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