Reference documentation and code samples for the Cloud AutoML V1 Client class ClassificationEvaluationMetrics.
Model evaluation metrics for classification problems.
Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.
Generated from protobuf message google.cloud.automl.v1.ClassificationEvaluationMetrics
Namespace
Google \ Cloud \ AutoMl \ V1Methods
__construct
Constructor.
data
array
Optional. Data for populating the Message object.
↳ au_prc
float
Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
↳ au_roc
float
Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
↳ log_loss
float
Output only. The Log Loss metric.
↳ confidence_metrics_entry
array< Google\Cloud\AutoMl\V1\ClassificationEvaluationMetrics\ConfidenceMetricsEntry
>
Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.
↳ confusion_matrix
Google\Cloud\AutoMl\V1\ClassificationEvaluationMetrics\ConfusionMatrix
Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.
↳ annotation_spec_id
array
Output only. The annotation spec ids used for this evaluation.
getAuPrc
Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
float
setAuPrc
Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
var
float
$this
getAuRoc
Output only. The Area Under Receiver Operating Characteristic curve metric.
Micro-averaged for the overall evaluation.
float
setAuRoc
Output only. The Area Under Receiver Operating Characteristic curve metric.
Micro-averaged for the overall evaluation.
var
float
$this
getLogLoss
Output only. The Log Loss metric.
float
setLogLoss
Output only. The Log Loss metric.
var
float
$this
getConfidenceMetricsEntry
Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE.
ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.
setConfidenceMetricsEntry
Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE.
ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.
$this
getConfusionMatrix
Output only. Confusion matrix of the evaluation.
Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.
hasConfusionMatrix
clearConfusionMatrix
setConfusionMatrix
Output only. Confusion matrix of the evaluation.
Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.
$this
getAnnotationSpecId
Output only. The annotation spec ids used for this evaluation.
setAnnotationSpecId
Output only. The annotation spec ids used for this evaluation.
var
string[]
$this
