Interpretable Machine Learning (A Guide for Making Black Box Models Explainable)

23 de June de 2020

 🔘 Table of associated records

<meta name="Description" CONTENT="Artificial Intelligence Journal" />
<meta name="r0identifier" content="b129021066d4fc15a561e0053c355588" />
RxRegistration IDNº
R0Hash MD5 (of R3):b129021066d4fc15a561e0053c355588
R1Registration number (in the domain editorialia.com at WordPress):dmeditorialiawp.12669
R2Date-p-order (ddmmyyyypx): 23062020p1
R3Cid (combined id R1+R2):dmeditorialiawp.1266923062020p1
R4Resource official title:Interpretable Machine Learning A Guide for Making Black Box Models Explainable
R5Publisher:Self-published promotion version
R6Resource website (1) ( #OpenAccess | #Openscience ): christophm.github.io/interpretable-ml-book/index.html
R12Authors (separated by commas):Christoph Molnar
R14Keyword (selected 1 among the labels applied to this entry):=ethics
R15QR code (of the linked url at WP):
R16Time stamp URL:
R17Digital signature URL:Pending signature
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