[][src]Struct google_bigquery2::RankingMetrics

pub struct RankingMetrics {
    pub mean_average_precision: Option<f64>,
    pub mean_squared_error: Option<f64>,
    pub average_rank: Option<f64>,
    pub normalized_discounted_cumulative_gain: Option<f64>,
}

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

This type is not used in any activity, and only used as part of another schema.

Fields

mean_average_precision: Option<f64>

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

mean_squared_error: Option<f64>

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.

average_rank: Option<f64>

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

normalized_discounted_cumulative_gain: Option<f64>

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.

Trait Implementations

impl Clone for RankingMetrics[src]

impl Debug for RankingMetrics[src]

impl Default for RankingMetrics[src]

impl<'de> Deserialize<'de> for RankingMetrics[src]

impl Part for RankingMetrics[src]

impl Serialize for RankingMetrics[src]

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