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.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0; } 2 1 I have a directory with over 400 GiB of data in it. I wanted to check that all the files can be read without errors, so a simple way I thought of was to tar it into /dev/null . But instead I see the following behavior: $ time tar cf /dev/null . real 0m4.387s user 0m3.462s sys 0m0.185s $ time tar cf - . > /dev/null real 0m3.130s user 0m3.091s sys 0m0.035s $ time tar cf - . | cat > /dev/null ^C real 10m32.985s user 0m1.942s sys 0m33.764s The third command above was forcibly stopped by Ctrl + C after having run for quite long already. Moreover, while the first two commands were working, activity indicato

Willelmus Tyrensis historiam scribens: e libro manu scripto saeculi 13 Willelmus seu Guillelmus Tyrensis [1] (natus circa 1130 in Palaestina; mortuus die 29 Septembris 1186) [2] fuit archiepiscopus Tyrensis et historicus rerum Palaestinae et Antiochiae. Index 1 Educatio et eruditio 2 Vita civilis et ecclesiastica 3 Opera 4 Notae 5 Bibliographia Educatio et eruditio | Balduinus puer (rex Balduinus IV futurus) cum contubernalibus et tutore Willelmo Tyrensi. Estoire de Eracles : Bibliotheca Britannica MS Yates Thompson 12 (saec. XIII) Circa annum 1130 Hierosolymis natus est parentibus burgensibus Francogallicis aut Italicis qui ad Regnum Hierosolymitanum post primam expeditionem sacram migraverant, quibus etiam alter filius, Radulphus, mercator in Regno erat. Puer Willelmus Hierosolymis educatus est, praecipue Latine sed fortasse Graece ac Arabice, et fieri potest ut unus ex eius condiscipulis futurus rex Balduinus III esset. Adulescens

0 $begingroup$ I have a dataset containing binary labels Y with values "correct" or "wrong". I want to study the reason of wrong state, based on several conditions made from multiple features. For example I want to know if "wrong" was caused by a "common wrong answer" or by "difficulty to answer", i.e. I want to get Y with 3 labels "correct ", "common wrong" and "difficult". Is this possible to make by using machine learning model such as logistic regression ? If not, is there any viable approach to this problem? machine-learning dataset logistic-regression prediction share | improve this question