GoogleApi.BigQuery.V2.Model.RankingMetrics (google_api_big_query v0.70.1) View Source

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


  • averageRank (type: float(), default: nil) - Determines the goodness of a ranking by computing the percentile rank from the predicted confidence and dividing it by the original rank.
  • meanAveragePrecision (type: float(), default: nil) - Calculates a precision per user for all the items by ranking them and then averages all the precisions across all the users.
  • meanSquaredError (type: float(), default: nil) - 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.
  • normalizedDiscountedCumulativeGain (type: float(), default: nil) - 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.

Link to this section Summary


Unwrap a decoded JSON object into its complex fields.

Link to this section Types


t() :: %GoogleApi.BigQuery.V2.Model.RankingMetrics{
  averageRank: float() | nil,
  meanAveragePrecision: float() | nil,
  meanSquaredError: float() | nil,
  normalizedDiscountedCumulativeGain: float() | nil

Link to this section Functions


decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.