Evaluation Evaluation metrics: Precision@K
{"Image DocumentName": "Evaluation Evaluation metrics:", "Image ImageDescription": "Spearman\u2019s rank correlation coefficient. p - Spearman\u2019s coefficient of rank correlation -1 \u2264 ps \u2264 1. di - is the different between the rank of xi and the rank of yi. n:- the number of data points in the sample (total number of observations) ps = - 1 or 1 high degree of correlation between x any y. Ps = 0 a lack of linear association between two variables Perfect Positive. Correlation. No Correlation. Perfect Negative. computes for a given result of ranked users, the fraction of relevant results in the top K results. The higher the precision, the better. the performance is. We use this metric to compare the results of the expert ranking algorithms that we developed with the ranking of experts resulted by counting the number of fair votes. Spearman\u2019s rank correlation coefficien is a non-parametric measure of statistical dependence between two ranked lists. Spearman\u2019s rank correlation coefficient it is based on rank order of scores and not the score data. Correlation Coefficient between the ranked variables d= Difference of rank between paired item in two series (lists).", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}