pub struct ClassifierEvaluationMetrics {
pub accuracy: Option<f64>,
pub f1_score: Option<f64>,
pub hamming_loss: Option<f64>,
pub micro_f1_score: Option<f64>,
pub micro_precision: Option<f64>,
pub micro_recall: Option<f64>,
pub precision: Option<f64>,
pub recall: Option<f64>,
}
Expand description
Describes the result metrics for the test data associated with an documentation classifier.
Fields
accuracy: Option<f64>
The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.
f1_score: Option<f64>
A measure of how accurate the classifier results are for the test data. It is derived from the Precision
and Recall
values. The F1Score
is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.
hamming_loss: Option<f64>
Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.
micro_f1_score: Option<f64>
A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision
and Micro Recall
values. The Micro F1Score
is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
micro_precision: Option<f64>
A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.
micro_recall: Option<f64>
A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.
precision: Option<f64>
A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.
recall: Option<f64>
A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.
Trait Implementations
sourceimpl Clone for ClassifierEvaluationMetrics
impl Clone for ClassifierEvaluationMetrics
sourcefn clone(&self) -> ClassifierEvaluationMetrics
fn clone(&self) -> ClassifierEvaluationMetrics
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for ClassifierEvaluationMetrics
impl Debug for ClassifierEvaluationMetrics
sourceimpl Default for ClassifierEvaluationMetrics
impl Default for ClassifierEvaluationMetrics
sourcefn default() -> ClassifierEvaluationMetrics
fn default() -> ClassifierEvaluationMetrics
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for ClassifierEvaluationMetrics
impl<'de> Deserialize<'de> for ClassifierEvaluationMetrics
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl PartialEq<ClassifierEvaluationMetrics> for ClassifierEvaluationMetrics
impl PartialEq<ClassifierEvaluationMetrics> for ClassifierEvaluationMetrics
sourcefn eq(&self, other: &ClassifierEvaluationMetrics) -> bool
fn eq(&self, other: &ClassifierEvaluationMetrics) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &ClassifierEvaluationMetrics) -> bool
fn ne(&self, other: &ClassifierEvaluationMetrics) -> bool
This method tests for !=
.
impl StructuralPartialEq for ClassifierEvaluationMetrics
Auto Trait Implementations
impl RefUnwindSafe for ClassifierEvaluationMetrics
impl Send for ClassifierEvaluationMetrics
impl Sync for ClassifierEvaluationMetrics
impl Unpin for ClassifierEvaluationMetrics
impl UnwindSafe for ClassifierEvaluationMetrics
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more