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

 🔘 Table of associated records

<meta name="Description" CONTENT="Artificial Intelligence Journal" /&gt;
<meta name="r0identifier" content="b129021066d4fc15a561e0053c355588" /&gt;

Rx	Registration ID	Nº
R0	Hash MD5 (of R3):	b129021066d4fc15a561e0053c355588
R1	Registration number (in the domain editorialia.com at WordPress):	dmeditorialiawp.12669
R2	Date-p-order (ddmmyyyypx):	23062020p1
R3	Cid (combined id R1+R2):	dmeditorialiawp.1266923062020p1
R4	Resource official title:	Interpretable Machine Learning A Guide for Making Black Box Models Explainable
R5	Publisher:	Self-published promotion version
R6	Resource website (1) ( #OpenAccess | #Openscience ):	christophm.github.io/interpretable-ml-book/index.html
R12	Authors (separated by commas):	Christoph Molnar
R14	Keyword (selected 1 among the labels applied to this entry):	=ethics
R15	QR code (of the linked url at WP):
R16	Time stamp URL:
R17	Digital signature URL:	Pending signature