class Google::Apis::BigqueryV2::RankingMetrics
Evaluation metrics used by weighted-ALS models specified by feedback_type= implicit.
Attributes
Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank. Corresponds to the JSON property `averageRank` @return [Float]
Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users. Corresponds to the JSON property `meanAveragePrecision` @return [Float]
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. Corresponds to the JSON property `meanSquaredError` @return [Float]
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. Corresponds to the JSON property `normalizedDiscountedCumulativeGain` @return [Float]
Public Class Methods
# File lib/google/apis/bigquery_v2/classes.rb, line 5514 def initialize(**args) update!(**args) end
Public Instance Methods
Update properties of this object
# File lib/google/apis/bigquery_v2/classes.rb, line 5519 def update!(**args) @average_rank = args[:average_rank] if args.key?(:average_rank) @mean_average_precision = args[:mean_average_precision] if args.key?(:mean_average_precision) @mean_squared_error = args[:mean_squared_error] if args.key?(:mean_squared_error) @normalized_discounted_cumulative_gain = args[:normalized_discounted_cumulative_gain] if args.key?(:normalized_discounted_cumulative_gain) end