Score breakdown

A Few Useful Things to Know About Machine Learning

paper-0059 · paper · 2012

Pedro Domingos

The most-shared practical wisdom paper in ML.

Abstract

Tapping into the "folk knowledge" needed to advance machine learning applications. [OpenAlex]

Academic, score -0.1677

MetricStatusValueNorm.WeightContributionSourceConfidenceLicenseProvenance
citation_countpresent3250.00.0146250.50.007313OpenAlexhighOpenAlex, CC0 metadatalink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.1recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.050.05OpenAlexmediumOpenAlex, CC0 metadatalink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed

Broad Influence, score 0.2029

MetricStatusValueNorm.WeightContributionSourceConfidenceLicenseProvenance
citation_countpresent3250.00.0146250.20.002925OpenAlexhighOpenAlex, CC0 metadatalink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.40.4OpenAlexmediumOpenAlex, CC0 metadatalink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.075recorded as missing; penalized by rule, never imputed

Governance Practitioner, score -0.2213

MetricStatusValueNorm.WeightContributionSourceConfidenceLicenseProvenance
citation_countpresent3250.00.0146250.250.003656OpenAlexhighOpenAlex, CC0 metadatalink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.15recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.10.1OpenAlexmediumOpenAlex, CC0 metadatalink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.175recorded as missing; penalized by rule, never imputed

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