Struct google_api_proto::google::cloud::automl::v1beta1::ClassificationEvaluationMetrics

pub struct ClassificationEvaluationMetrics {
    pub au_prc: f32,
    pub base_au_prc: f32,
    pub au_roc: f32,
    pub log_loss: f32,
    pub confidence_metrics_entry: Vec<ConfidenceMetricsEntry>,
    pub confusion_matrix: Option<ConfusionMatrix>,
    pub annotation_spec_id: Vec<String>,
}

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of “segment_classification” type.

Fields

au_prc: f32

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

base_au_prc: f32

👎Deprecated

Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.

au_roc: f32

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

log_loss: f32

Output only. The Log Loss metric.

confidence_metrics_entry: Vec<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: Option<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: Vec<String>

Output only. The annotation spec ids used for this evaluation.

Trait Implementations

impl Clone for ClassificationEvaluationMetrics

fn clone(&self) -> ClassificationEvaluationMetrics

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ClassificationEvaluationMetrics

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ClassificationEvaluationMetrics

fn default() -> Self

Returns the “default value” for a type.

impl Message for ClassificationEvaluationMetrics

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for ClassificationEvaluationMetrics

fn eq(&self, other: &ClassificationEvaluationMetrics) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ClassificationEvaluationMetrics