Struct tangram_metrics::ClassMetrics [−][src]
pub struct ClassMetrics { pub true_positives: u64, pub false_positives: u64, pub true_negatives: u64, pub false_negatives: u64, pub accuracy: f32, pub precision: f32, pub recall: f32, pub f1_score: f32, }
Expand description
ClassMetrics are class specific metrics used to evaluate the model’s performance on each individual class.
Fields
true_positives: u64
This is the total number of examples whose label is equal to this class that the model predicted as belonging to this class.
false_positives: u64
This is the total number of examples whose label is not equal to this class that the model predicted as belonging to this class.
true_negatives: u64
This is the total number of examples whose label is not equal to this class that the model predicted as not belonging to this class.
false_negatives: u64
This is the total number of examples whose label is equal to this class that the model predicted as not belonging to this class.
accuracy: f32
The accuracy is the fraction of examples of this class that were correctly classified.
precision: f32
The precision is the fraction of examples the model predicted as belonging to this class whose label is actually equal to this class. precision = true_positives / (true_positives + false_positives)
. See Precision and Recall.
recall: f32
The recall is the fraction of examples in the dataset whose label is equal to this class that the model predicted as equal to this class. recall = true_positives / (true_positives + false_negatives)
.
f1_score: f32
The f1 score is the harmonic mean of the precision and the recall. See F1 Score.
Trait Implementations
Auto Trait Implementations
impl RefUnwindSafe for ClassMetrics
impl Send for ClassMetrics
impl Sync for ClassMetrics
impl Unpin for ClassMetrics
impl UnwindSafe for ClassMetrics