package cortex.api.job.tabular

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Used in: BinaryClassificationTrainSummary, EvaluationResult

• optional ClassificationSummary general_classification_summary = 1

• double ks = 3

Used in: TrainResult

• double area_under_roc = 1

• repeated RocValue roc_values = 2

• double f1_score = 3

• double precision = 4

• double recall = 5

• double threshold = 6

• optional BinaryClassificationEvalSummary binary_classification_eval_summary = 7

Used in: ClassificationSummary

• string class_name = 1

• int32 true_positive = 2

• int32 true_negative = 3

• int32 false_positive = 4

• int32 false_negative = 5

Used in: BinaryClassificationEvalSummary, EvaluationResult, TrainResult

• repeated ClassConfusion confusion_matrix = 1

Used in: EvaluateRequest, PredictRequest

• string train_name = 1

• string current_name = 2

• string model_id = 1

• optional table.DataSource input = 2

• repeated ColumnMapping predictors = 3

• optional ColumnMapping response = 4

• optional ColumnMapping weight = 5

• optional table.DataSource output = 6

• bool drop_previous_result_table = 7

• string prediction_result_column_name = 8

• repeated table.ProbabilityClassColumn probability_columns = 9

• optional common.ClassReference model_reference = 10

• oneof summary

  • ClassificationSummary classification_summary = 1

  • BinaryClassificationEvalSummary binary_classification_eval_summary = 2

  • RegressionSummary regression_summary = 3

• optional table.DataSource output = 4

Used in: TrainResult

• REGRESSION = 0

• BINARY = 1

• MULTICLASS = 2

Used in: PredictorSummary

• double coefficient = 1

• double std_err = 2

• double t_value = 3

• double p_value = 4

• string model_id = 1

• optional table.DataSource input = 2

• optional table.DataSource output = 3

• repeated ColumnMapping predictors = 4

• bool drop_previous_result_table = 5

• string prediction_result_column_name = 6

• repeated table.ProbabilityClassColumn probability_columns = 7

• optional common.ClassReference model_reference = 8

• repeated string class_probability_column_names = 1

• optional table.DataSource output = 2

Used in: TrainResult

• string name = 1

• oneof summary

  • ParametricModelPredictorSummary parametric_model_predictor_summary = 2

  • TreeModelPredictorSummary tree_model_predictor_summary = 3

Used in: EvaluationResult, TrainResult

• double rmse = 1

• double r2 = 2

• double mape = 3

Used in: BinaryClassificationTrainSummary

• double false_positive = 1

• double true_positive = 2

• string path = 1

• optional common.ClassReference model_class_reference = 2

• optional common.ModelReference tabular_model_reference = 1

• optional table.DataSource input = 1

• repeated table.TableColumn predictors = 2

• optional table.TableColumn response = 3

• optional table.TableColumn weight = 4

• optional table.DataSource output = 5

• bool drop_previous_result_table = 6

• string prediction_result_column_name = 7

• optional google.protobuf.StringValue probability_columns_prefix = 8

• string model_id = 1

• ModelType model_type = 2

• string formula = 3

• oneof summary

  • ClassificationSummary classification_summary = 4

  • BinaryClassificationTrainSummary binary_classification_train_summary = 5

  • RegressionSummary regression_summary = 6

• repeated PredictorSummary predictors_summary = 7

• repeated table.ProbabilityClassColumn probability_columns = 8

• optional table.DataSource output = 9

• string model_primitive = 10

• string model_file_path = 11

Used in: PredictorSummary

• double importance = 1