Struct 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 duplicate 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() -> AggregateClassificationMetrics

Returns the “default value” for a type.

impl Message for AggregateClassificationMetrics

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_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 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 AggregateClassificationMetrics

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for AggregateClassificationMetrics