Package | Description |
---|---|
com.google.api.services.bigquery.model |
Modifier and Type | Method and Description |
---|---|
RankingMetrics |
RankingMetrics.clone() |
RankingMetrics |
EvaluationMetrics.getRankingMetrics()
Populated for implicit feedback type matrix factorization models.
|
RankingMetrics |
RankingMetrics.set(String fieldName,
Object value) |
RankingMetrics |
RankingMetrics.setAverageRank(Double averageRank)
Determines the goodness of a ranking by computing the percentile rank from the predicted
confidence and dividing it by the original rank.
|
RankingMetrics |
RankingMetrics.setMeanAveragePrecision(Double meanAveragePrecision)
Calculates a precision per user for all the items by ranking them and then averages all the
precisions across all the users.
|
RankingMetrics |
RankingMetrics.setMeanSquaredError(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.
|
RankingMetrics |
RankingMetrics.setNormalizedDiscountedCumulativeGain(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.
|
Modifier and Type | Method and Description |
---|---|
EvaluationMetrics |
EvaluationMetrics.setRankingMetrics(RankingMetrics rankingMetrics)
Populated for implicit feedback type matrix factorization models.
|
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