Struct gapi_grpc::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

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.

pub 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() -> ClassificationEvaluationMetrics

Returns the “default value” for a type.

impl Message for ClassificationEvaluationMetrics

fn encode_raw<B>(&self, buf: &mut B) where
    B: BufMut, 

fn merge_field<B>(
    &mut self,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

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.

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

Encodes the message to a buffer.

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

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

pub fn decode<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes an instance of the message from a buffer.

pub fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

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

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

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

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

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

impl PartialEq<ClassificationEvaluationMetrics> 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: &ClassificationEvaluationMetrics) -> bool

This method tests for !=.

impl StructuralPartialEq for ClassificationEvaluationMetrics