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Class RankingMetrics

Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.

Inheritance
object
RankingMetrics
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 RankingMetrics : IDirectResponseSchema

Properties

AverageRank

Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.

Declaration
[JsonProperty("averageRank")]
public virtual double? AverageRank { 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

MeanAveragePrecision

Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.

Declaration
[JsonProperty("meanAveragePrecision")]
public virtual double? MeanAveragePrecision { get; set; }
Property Value
Type Description
double?

MeanSquaredError

Similar to the mean squared error computed in regression and explicit recommendation models except instead of computing the rating directly, the output from evaluate is computed against a preference which is 1 or 0 depending on if the rating exists or not.

Declaration
[JsonProperty("meanSquaredError")]
public virtual double? MeanSquaredError { get; set; }
Property Value
Type Description
double?

NormalizedDiscountedCumulativeGain

A metric to determine the goodness of a ranking calculated from the predicted confidence by comparing it to an ideal rank measured by the original ratings.

Declaration
[JsonProperty("normalizedDiscountedCumulativeGain")]
public virtual double? NormalizedDiscountedCumulativeGain { get; set; }
Property Value
Type Description
double?

Implements

IDirectResponseSchema
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