Class: RankingMetrics

eclairjs/mllib/evaluation.RankingMetrics

new RankingMetrics(predictionAndLabels)

Parameters:
Name Type Description
predictionAndLabels module:eclairjs/rdd.RDD
Source:

Methods

(static) of(predictionAndLabels) → {module:eclairjs/mllib/evaluation.RankingMetrics}

Creates a RankingMetrics instance (for Java users).
Parameters:
Name Type Description
predictionAndLabels module:eclairjs/rdd.RDD a RDD of (predicted ranking, ground truth set) pairs
Source:
Returns:
Type
module:eclairjs/mllib/evaluation.RankingMetrics

ndcgAt(k) → {Promise.<number>}

Compute the average NDCG value of all the queries, truncated at ranking position k. The discounted cumulative gain at position k is computed as: sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1), and the NDCG is obtained by dividing the DCG value on the ground truth set. In the current implementation, the relevance value is binary. If a query has an empty ground truth set, zero will be used as ndcg together with a log warning. See the following paper for detail: IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
Parameters:
Name Type Description
k number the position to compute the truncated ndcg, must be positive
Source:
Returns:
the average ndcg at the first k ranking positions
Type
Promise.<number>

precisionAt(k) → {Promise.<number>}

Compute the average precision of all the queries, truncated at ranking position k. If for a query, the ranking algorithm returns n (n < k) results, the precision value will be computed as #(relevant items retrieved) / k. This formula also applies when the size of the ground truth set is less than k. If a query has an empty ground truth set, zero will be used as precision together with a log warning. See the following paper for detail: IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
Parameters:
Name Type Description
k number the position to compute the truncated precision, must be positive
Source:
Returns:
the average precision at the first k ranking positions
Type
Promise.<number>