Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 1
The statistics are computed given the following assumptions about the data distribution:
The library uses the following quality metrics:
Quality Metric |
Definition |
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Root Mean Square (RMS) Error |
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Vector of variances
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A set of variance-covariance matrices C = C 1, ..., C k for vectors of betas β jt , j = 1, ..., k |
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Z-score statistics used in testing of insignificance of a single coefficient β jt |
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Confidence interval for β jt |
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The library uses the following quality metrics:
Quality Metric |
Definition |
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Mean of expected responses, ERM = (ERM 1, ..., ERM k ) |
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Variance of expected responses, ERV = (ERV 1, ..., ERV k ) |
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Regression Sum of Squares RegSS = (RegSS 1, ..., RegSS k ) |
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Sum of Squares of Residuals ResSS = (ResSS 1, ..., ResSS k ) |
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Total Sum of Squares TSS = (TSS 1, ..., TSS k ) |
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Determination Coefficient ![]() |
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F-statistics used in testing insignificance of a group of betas F = (F 1, ..., F k ) |
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