Score breakdown

Random Forests

paper-0043 · paper · 2001

Leo Breiman

The most-used classical ML algorithm in applied practice.

Academic, score 0.1092

MetricStatusValueNorm.WeightContributionSourceConfidenceLicenseProvenance
citation_countpresent126295.00.56850.50.28425OpenAlexhighOpenAlex, 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 0.3137

MetricStatusValueNorm.WeightContributionSourceConfidenceLicenseProvenance
citation_countpresent126295.00.56850.20.1137OpenAlexhighOpenAlex, 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 -0.0829

MetricStatusValueNorm.WeightContributionSourceConfidenceLicenseProvenance
citation_countpresent126295.00.56850.250.142125OpenAlexhighOpenAlex, 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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