Class AggregateClassificationMetrics

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.

Inheritance

object

AggregateClassificationMetrics

Implements

IDirectResponseSchema

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Namespace: Google.Apis.Bigquery.v2.Data

Assembly: Google.Apis.Bigquery.v2.dll

Syntax

public class AggregateClassificationMetrics : IDirectResponseSchema

Properties

Accuracy

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

Declaration

[JsonProperty("accuracy")]
public virtual double? Accuracy { get; set; }

Property Value

Type	Description
double?

ETag

The ETag of the item.

Declaration

public virtual string ETag { get; set; }

Property Value

Type	Description
string

F1Score

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

Declaration

[JsonProperty("f1Score")]
public virtual double? F1Score { get; set; }

Property Value

Type	Description
double?

LogLoss

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

Declaration

[JsonProperty("logLoss")]
public virtual double? LogLoss { get; set; }

Property Value

Type	Description
double?

Precision

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.

Declaration

[JsonProperty("precision")]
public virtual double? Precision { get; set; }

Property Value

Type	Description
double?

Recall

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

Declaration

[JsonProperty("recall")]
public virtual double? Recall { get; set; }

Property Value

Type	Description
double?

RocAuc

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

Declaration

[JsonProperty("rocAuc")]
public virtual double? RocAuc { get; set; }

Property Value

Type	Description
double?

Threshold

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.

Declaration

[JsonProperty("threshold")]
public virtual double? Threshold { get; set; }

Property Value

Type	Description
double?

Implements

IDirectResponseSchema