Metrics and time information

Contains

The metric values for the training and test sets.

The table below lists the names of parameters that define the metric values to output. The values of all functions defined by these parameters are output.

Command-line version parameters Python parameters R parameters

--custom-metric custom_metric custom_loss

--loss-function loss-function loss-function

--eval-metric eval-metric eval-metric
Information about the number of seconds of training:
- passed since the beginning
- remaining until the end

Command-line version parameters	Python parameters	R parameters
`--custom-metric`	`custom_metric`	`custom_loss`
`--loss-function`	`loss-function`	`loss-function`
`--eval-metric`	`eval-metric`	`eval-metric`

Format

The resulting JSON file consists of the following arrays:

meta

Contains basic information about the training.

Format of the array with prettified sample data:

"meta": {
    "`launch_mode`": "Train",
    "`name`": "second",
    "`iteration_count`": 1000,
    "`learn_metrics`": [
      {
        "`name`": "Precision:class=0",
        "`value`": "Max"
      },
      {
        "`name`": "Precision:class=1",
        "`value`": "Max"
      }
    ],
    "`test_sets`": [
      "test",
      ...
      "testN"
    ],
    "`test_metrics`": [
      {
        "`name`":"Precision:class=0",
        "`value`":"Max"
      },
      {
        "`name`":"Precision:class=1",
        "`value`":"Max"
      }
    ],
    "`learn_sets`": [
      "learn"
    ]
}

Property	Type	Description
`launch_mode`	string	The specified launch mode. Possible values: - Train — Training launch mode. - CV — Cross-validation launch mode (for the Python cv method only). The command-line implementation of the Cross-validation feature returns the Train value in this parameter.
`name`	string	The experiment name. The value can be set in the `--name` (`--name`) training parameter. The default name is .
`iteration_count`	int	The maximum number of trees that can be built when solving machine learning problems. The final number of iterations may be less than the output in this property.
`learn_metrics`	array	A list of metrics calculated for the learning dataset and information regarding the optimization method.
`test_sets`	array	The names of the arrays within the iterations array that contain the calculated values of metrics for the validation datasets.
`test_metrics`	array	A list of metrics calculated for the validation dataset and information regarding the optimization method.
`name`	string	The name of the metric.
`value`	string	The method for defining the best value of the metric. Possible values: - Min — The smaller the value of the metric, the better. - Max — The bigger the value of the metric, the better. - Undefined — The best value of the metric is not defined. - Float value — The best value of the metric is user-defined.
`learn_sets`	array	The name of the array within the iterations array that contains the calculated values of the metrics for the learning dataset.

iterations

Contains an array of metric values for the training and test sets and information on the duration of training for each iteration.