Struct googapis::google::cloud::bigquery::v2::model::AggregateClassificationMetrics

pub struct AggregateClassificationMetrics {
    pub precision: Option<f64>,
    pub recall: Option<f64>,
    pub accuracy: Option<f64>,
    pub threshold: Option<f64>,
    pub f1_score: Option<f64>,
    pub log_loss: Option<f64>,
    pub roc_auc: Option<f64>,
}

Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.

Fields

precision: Option<f64>

Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.

recall: Option<f64>

Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.

accuracy: Option<f64>

Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.

threshold: Option<f64>

Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.

f1_score: Option<f64>

The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.

log_loss: Option<f64>

Logarithmic Loss. For multiclass this is a macro-averaged metric.

roc_auc: Option<f64>

Area Under a ROC Curve. For multiclass this is a macro-averaged metric.

Trait Implementations

impl Clone for AggregateClassificationMetrics

fn clone(&self) -> AggregateClassificationMetrics

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AggregateClassificationMetrics

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

Formats the value using the given formatter.

impl Default for AggregateClassificationMetrics

fn default() -> Self

Returns the “default value” for a type.

impl Message for AggregateClassificationMetrics

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.

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

Encodes the message to a buffer.

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

Encodes the message to a newly allocated buffer.

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

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

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

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

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

Decodes an instance of the message from a buffer.

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.

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.

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<AggregateClassificationMetrics> for AggregateClassificationMetrics

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

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

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

This method tests for !=.

impl StructuralPartialEq for AggregateClassificationMetrics