Notebooks Coverage¶
Report on last executions.
61% 2022-05-27

index |
coverage |
exe time |
last execution |
name |
title |
success |
time |
nb cells |
nb runs |
nb valid |
---|---|---|---|---|---|---|---|---|---|---|
0 |
100% |
22.379 |
2022-05-27 |
1A.1 - Liste, tuple, ensemble, dictionnaire, liste chaînée, coût des opérations |
True |
25.630 |
4 |
4 |
4 |
|
1 |
100% |
8.580 |
2022-05-27 |
True |
11.823 |
12 |
12 |
12 |
||
2 |
100% |
8.120 |
2022-05-27 |
True |
11.679 |
23 |
23 |
23 |
||
3 |
100% |
1.296 |
2022-05-27 |
1A.algo - Calculer le nombre de façons de monter une échelle. |
True |
4.849 |
6 |
6 |
6 |
|
4 |
100% |
4.242 |
2022-05-27 |
True |
7.791 |
16 |
16 |
16 |
||
5 |
100% |
6.586 |
2022-05-27 |
True |
10.093 |
15 |
15 |
15 |
||
6 |
100% |
0.884 |
2022-05-27 |
True |
4.418 |
4 |
4 |
4 |
||
7 |
100% |
1.145 |
2022-05-27 |
True |
4.346 |
8 |
8 |
8 |
||
8 |
100% |
45.944 |
2022-05-27 |
True |
49.325 |
15 |
15 |
15 |
||
9 |
100% |
12.807 |
2022-05-27 |
True |
15.968 |
11 |
11 |
11 |
||
10 |
100% |
2.220 |
2022-05-27 |
True |
5.621 |
9 |
9 |
9 |
||
11 |
100% |
8.150 |
2022-05-27 |
True |
11.454 |
29 |
29 |
29 |
||
12 |
100% |
2.538 |
2022-05-27 |
True |
5.774 |
8 |
8 |
8 |
||
13 |
100% |
56.376 |
2022-05-27 |
True |
59.642 |
17 |
17 |
17 |
||
14 |
0% |
nan |
nan |
29 |
0 |
|||||
15 |
0% |
nan |
nan |
24 |
0 |
|||||
16 |
0% |
nan |
nan |
35 |
0 |
|||||
17 |
0% |
nan |
nan |
33 |
0 |
|||||
18 |
100% |
13.922 |
2022-05-27 |
True |
17.111 |
20 |
20 |
20 |
||
19 |
0% |
nan |
nan |
60 |
0 |
|||||
20 |
89% |
3.302 |
2022-05-27 |
True |
6.400 |
28 |
25 |
25 |
||
21 |
100% |
7.326 |
2022-05-27 |
True |
10.444 |
29 |
29 |
29 |
||
22 |
100% |
6.878 |
2022-05-27 |
True |
10.703 |
21 |
21 |
21 |
||
23 |
100% |
2.015 |
2022-05-27 |
True |
5.365 |
11 |
11 |
11 |
||
24 |
100% |
11.210 |
2022-05-27 |
True |
14.405 |
20 |
20 |
20 |
||
25 |
100% |
156.169 |
2022-05-27 |
True |
159.392 |
19 |
19 |
19 |
||
26 |
100% |
10.755 |
2022-05-27 |
True |
14.089 |
19 |
19 |
19 |
||
27 |
0% |
nan |
nan |
138 |
0 |
|||||
28 |
100% |
3.439 |
2022-05-27 |
True |
6.598 |
15 |
15 |
15 |
||
29 |
100% |
8.853 |
2022-05-27 |
True |
12.050 |
23 |
23 |
23 |
||
30 |
100% |
1.296 |
2022-05-27 |
True |
4.426 |
5 |
5 |
5 |
||
31 |
100% |
15.505 |
2022-05-27 |
True |
18.669 |
28 |
28 |
28 |
||
32 |
100% |
3.974 |
2022-05-27 |
True |
7.080 |
8 |
8 |
8 |
||
33 |
0% |
nan |
2A.i - Données non structurées, programmation fonctionnelle : dask |
nan |
28 |
0 |
||||
34 |
0% |
nan |
nan |
46 |
0 |
|||||
35 |
0% |
nan |
nan |
78 |
0 |
|||||
36 |
100% |
31.258 |
2022-05-27 |
True |
34.408 |
27 |
27 |
27 |
||
37 |
0% |
nan |
nan |
30 |
0 |
|||||
38 |
0% |
nan |
nan |
21 |
0 |
|||||
39 |
100% |
1.647 |
2022-05-27 |
1A.e - Correction de l’interrogation écrite du 26 septembre 2014 |
True |
4.780 |
12 |
12 |
12 |
|
40 |
100% |
3.436 |
2022-05-27 |
1A.e - Correction de l’interrogation écrite du 10 octobre 2014 |
True |
6.968 |
17 |
17 |
17 |
|
41 |
82% |
3.295 |
2022-05-27 |
1A.e - Correction de l’interrogation écrite du 14 novembre 2014 |
True |
6.529 |
35 |
29 |
29 |
|
42 |
100% |
1.954 |
2022-05-27 |
1A.e - Correction de l’interrogation écrite du 14 novembre 2014 |
True |
5.110 |
15 |
15 |
15 |
|
43 |
100% |
0.783 |
2022-05-27 |
1A.e - Correction de l’interrogation écrite du 26 septembre 2015 |
True |
3.971 |
12 |
12 |
12 |
|
44 |
100% |
1.366 |
2022-05-27 |
1A.e - Correction de l’interrogation écrite du 6 novembre 2015 |
True |
4.643 |
16 |
16 |
16 |
|
45 |
100% |
23.988 |
2022-05-27 |
1A.e - TD noté, 27 novembre 2012 (éléments de code pour le coloriage) |
True |
27.192 |
6 |
6 |
6 |
|
46 |
100% |
51.257 |
2022-05-27 |
True |
55.096 |
14 |
14 |
14 |
||
47 |
100% |
5.232 |
2022-05-27 |
True |
9.201 |
36 |
36 |
36 |
||
48 |
100% |
0.254 |
2022-05-27 |
True |
3.388 |
6 |
6 |
6 |
||
49 |
100% |
9.241 |
2022-05-27 |
True |
12.412 |
26 |
26 |
26 |
||
50 |
100% |
3.831 |
2022-05-27 |
True |
7.058 |
23 |
23 |
23 |
||
51 |
100% |
3.610 |
2022-05-27 |
True |
6.994 |
21 |
21 |
21 |
||
52 |
100% |
13.480 |
2022-05-27 |
True |
16.662 |
25 |
25 |
25 |
||
53 |
100% |
12.548 |
2022-05-27 |
True |
15.674 |
21 |
21 |
21 |
||
54 |
100% |
61.373 |
2022-05-27 |
True |
64.652 |
17 |
17 |
17 |
||
55 |
100% |
33.043 |
2022-05-27 |
True |
36.209 |
18 |
18 |
18 |
||
56 |
100% |
12.140 |
2022-05-27 |
True |
15.377 |
29 |
29 |
29 |
||
57 |
100% |
13.990 |
2022-05-27 |
True |
17.497 |
28 |
28 |
28 |
||
58 |
100% |
25.059 |
2022-05-27 |
True |
28.204 |
24 |
24 |
24 |
||
59 |
100% |
26.903 |
2022-05-27 |
True |
30.090 |
31 |
31 |
31 |
||
60 |
100% |
1.233 |
2022-05-27 |
True |
4.539 |
9 |
9 |
9 |
||
61 |
100% |
6.602 |
2022-05-27 |
True |
9.759 |
10 |
10 |
10 |
||
62 |
100% |
42.048 |
2022-05-27 |
True |
45.186 |
39 |
39 |
39 |
||
63 |
100% |
12.942 |
2022-05-27 |
True |
16.067 |
11 |
11 |
11 |
||
64 |
100% |
4.412 |
2022-05-27 |
True |
7.563 |
6 |
6 |
6 |
||
65 |
100% |
5.801 |
2022-05-27 |
True |
8.956 |
11 |
11 |
11 |
||
66 |
100% |
1.507 |
2022-05-27 |
3A.mr - Random Walk with Restart (système de recommandations) |
True |
4.648 |
4 |
4 |
4 |
|
67 |
0% |
nan |
nan |
18 |
0 |
|||||
68 |
100% |
26.150 |
2022-05-27 |
True |
29.351 |
14 |
14 |
14 |
||
69 |
100% |
39.540 |
2022-05-27 |
True |
42.675 |
15 |
15 |
15 |
||
70 |
0% |
nan |
nan |
30 |
0 |
|||||
71 |
0% |
nan |
nan |
53 |
0 |
|||||
72 |
0% |
nan |
nan |
54 |
0 |
|||||
73 |
100% |
6.360 |
2022-05-27 |
sklearn_ensae_course/00_introduction_machine_learning_and_data.ipynb |
True |
9.518 |
13 |
13 |
13 |
|
74 |
100% |
6.492 |
2022-05-27 |
2A.ML101.1: Introduction to data manipulation with scientific Python |
True |
9.638 |
18 |
18 |
18 |
|
75 |
100% |
9.189 |
2022-05-27 |
sklearn_ensae_course/02_basic_of_machine_learning_with_scikit-learn.ipynb |
2A.ML101.2: Basic principles of machine learning with scikit-learn |
True |
12.321 |
15 |
15 |
15 |
76 |
100% |
21.943 |
2022-05-27 |
2A.ML101.3: Supervised Learning: Classification of Handwritten Digits |
True |
25.059 |
12 |
12 |
12 |
|
77 |
100% |
11.353 |
2022-05-27 |
True |
14.492 |
12 |
12 |
12 |
||
78 |
100% |
17.989 |
2022-05-27 |
sklearn_ensae_course/05_measuring_prediction_performance.ipynb |
True |
21.379 |
23 |
23 |
23 |
|
79 |
100% |
17.477 |
2022-05-27 |
2A.ML101.6: Unsupervised Learning: Dimensionality Reduction and Visualization |
True |
20.600 |
18 |
18 |
18 |
|
80 |
100% |
19.562 |
2022-05-27 |
sklearn_ensae_course/07_application_to_face_recognition.ipynb |
True |
22.769 |
19 |
19 |
19 |
|
81 |
100% |
11.254 |
2022-05-27 |
sklearn_ensae_course/08_validation_and_learning_curves.ipynb |
True |
14.430 |
15 |
15 |
15 |
|
82 |
100% |
0.486 |
2022-05-27 |
True |
3.689 |
6 |
6 |
6 |
||
83 |
100% |
7.719 |
2022-05-27 |
True |
10.903 |
30 |
30 |
30 |
||
84 |
100% |
0.322 |
2022-05-27 |
True |
3.365 |
3 |
3 |
3 |
||
85 |
100% |
1.460 |
2022-05-27 |
True |
4.714 |
11 |
11 |
11 |
||
86 |
100% |
43.966 |
2022-05-27 |
True |
47.329 |
20 |
20 |
20 |
||
87 |
100% |
0.429 |
2022-05-27 |
True |
3.654 |
5 |
5 |
5 |
||
88 |
100% |
0.730 |
2022-05-27 |
True |
3.887 |
7 |
7 |
7 |
||
89 |
75% |
1.312 |
2022-05-27 |
True |
4.403 |
16 |
12 |
12 |
||
90 |
100% |
4.100 |
2022-05-27 |
True |
7.256 |
30 |
30 |
30 |
||
91 |
100% |
2.875 |
2022-05-27 |
True |
5.993 |
26 |
26 |
26 |
||
92 |
100% |
3.697 |
2022-05-27 |
True |
6.871 |
20 |
20 |
20 |
||
93 |
100% |
1.859 |
2022-05-27 |
1A.2 - Classes, méthodes, attributs, opérateurs et carré magique |
True |
5.006 |
17 |
17 |
17 |
|
94 |
100% |
1.080 |
2022-05-27 |
True |
4.203 |
11 |
11 |
11 |
||
95 |
100% |
1.486 |
2022-05-27 |
True |
4.585 |
13 |
13 |
13 |
||
96 |
100% |
0.840 |
2022-05-27 |
True |
3.931 |
8 |
8 |
8 |
||
97 |
100% |
1.071 |
2022-05-27 |
1A.1 - Dictionnaires, fonctions, code de Vigenère (correction) |
True |
4.454 |
9 |
9 |
9 |
|
98 |
100% |
6.032 |
2022-05-27 |
1A.2 - Modules, fichiers, expressions régulières (correction) |
True |
9.139 |
20 |
20 |
20 |
|
99 |
100% |
39.153 |
2022-05-27 |
1A.2 - Classes, méthodes, attributs, opérateurs et carré magique (correction) |
True |
42.282 |
12 |
12 |
12 |
|
100 |
100% |
0.594 |
2022-05-27 |
True |
3.784 |
5 |
5 |
5 |
||
101 |
100% |
3.216 |
2022-05-27 |
True |
6.337 |
5 |
5 |
5 |
||
102 |
100% |
5.292 |
2022-05-27 |
True |
8.452 |
11 |
11 |
11 |
||
103 |
100% |
1.496 |
2022-05-27 |
True |
4.623 |
6 |
6 |
6 |
||
104 |
100% |
13.407 |
2022-05-27 |
True |
16.590 |
23 |
23 |
23 |
||
105 |
100% |
0.396 |
2022-05-27 |
True |
3.537 |
4 |
4 |
4 |
||
106 |
100% |
1.581 |
2022-05-27 |
True |
4.732 |
8 |
8 |
8 |
||
107 |
100% |
1.916 |
2022-05-27 |
True |
5.466 |
9 |
9 |
9 |
||
108 |
100% |
1.063 |
2022-05-27 |
True |
4.155 |
6 |
6 |
6 |
||
109 |
100% |
1.251 |
2022-05-27 |
True |
4.504 |
6 |
6 |
6 |
||
110 |
100% |
2.167 |
2022-05-27 |
True |
5.339 |
11 |
11 |
11 |
||
111 |
100% |
0.143 |
2022-05-27 |
True |
3.301 |
4 |
4 |
4 |
||
112 |
100% |
3.902 |
2022-05-27 |
True |
7.031 |
17 |
17 |
17 |
||
113 |
100% |
0.487 |
2022-05-27 |
True |
3.668 |
8 |
8 |
8 |
||
114 |
100% |
0.497 |
2022-05-27 |
True |
3.635 |
10 |
10 |
10 |
||
115 |
100% |
0.145 |
2022-05-27 |
True |
3.250 |
4 |
4 |
4 |
||
116 |
100% |
0.702 |
2022-05-27 |
True |
3.816 |
6 |
6 |
6 |
||
117 |
100% |
14.054 |
2022-05-27 |
True |
17.539 |
17 |
17 |
17 |
||
118 |
100% |
6.285 |
2022-05-27 |
1A.algo - Programmation dynamique et plus court chemin (correction) |
True |
9.438 |
15 |
15 |
15 |
|
119 |
100% |
3.408 |
2022-05-27 |
True |
6.524 |
20 |
20 |
20 |
||
120 |
0% |
nan |
nan |
38 |
0 |
|||||
121 |
100% |
4.035 |
2022-05-27 |
True |
7.169 |
5 |
5 |
5 |
||
122 |
100% |
1.583 |
2022-05-27 |
True |
4.683 |
7 |
7 |
7 |
||
123 |
100% |
0.000 |
2022-05-27 |
True |
3.140 |
1 |
1 |
1 |
||
124 |
100% |
104.208 |
2022-05-27 |
1A.algo - la plus grande sous-séquence croissante - correction |
True |
107.379 |
10 |
10 |
10 |
|
125 |
87% |
2.417 |
2022-05-27 |
True |
5.508 |
8 |
7 |
7 |
||
126 |
100% |
3.719 |
2022-05-27 |
True |
6.862 |
10 |
10 |
10 |
||
127 |
100% |
1.455 |
2022-05-27 |
True |
4.681 |
6 |
6 |
6 |
||
128 |
100% |
17.611 |
2022-05-27 |
True |
20.802 |
8 |
8 |
8 |
||
129 |
100% |
0.138 |
2022-05-27 |
True |
3.538 |
7 |
7 |
7 |
||
130 |
100% |
1.701 |
2022-05-27 |
1A.data - Décorrélation de variables aléatoires - correction |
True |
4.827 |
15 |
15 |
15 |
|
131 |
100% |
8.295 |
2022-05-27 |
True |
11.499 |
51 |
51 |
51 |
||
132 |
0% |
nan |
nan |
38 |
0 |
|||||
133 |
0% |
nan |
nan |
17 |
0 |
|||||
134 |
0% |
nan |
nan |
29 |
0 |
|||||
135 |
0% |
nan |
nan |
28 |
0 |
|||||
136 |
0% |
nan |
nan |
24 |
0 |
|||||
137 |
100% |
15.606 |
2022-05-27 |
True |
18.730 |
23 |
23 |
23 |
||
138 |
100% |
44.711 |
2022-05-27 |
True |
47.919 |
33 |
33 |
33 |
||
139 |
100% |
3.899 |
2022-05-27 |
True |
7.245 |
15 |
15 |
15 |
||
140 |
100% |
20.466 |
2022-05-27 |
True |
23.777 |
15 |
15 |
15 |
||
141 |
100% |
8.846 |
2022-05-27 |
True |
12.057 |
45 |
45 |
45 |
||
142 |
100% |
31.688 |
2022-05-27 |
True |
34.915 |
35 |
35 |
35 |
||
143 |
100% |
3.708 |
2022-05-27 |
True |
6.932 |
11 |
11 |
11 |
||
144 |
100% |
22.680 |
2022-05-27 |
True |
25.942 |
64 |
64 |
64 |
||
145 |
100% |
31.865 |
2022-05-27 |
True |
35.057 |
18 |
18 |
18 |
||
146 |
100% |
1.402 |
2022-05-27 |
True |
4.494 |
14 |
14 |
14 |
||
147 |
100% |
13.348 |
2022-05-27 |
True |
16.666 |
28 |
28 |
28 |
||
148 |
100% |
4.756 |
2022-05-27 |
True |
8.030 |
24 |
24 |
24 |
||
149 |
100% |
16.008 |
2022-05-27 |
True |
19.400 |
16 |
16 |
16 |
||
150 |
100% |
41.502 |
2022-05-27 |
True |
44.680 |
32 |
32 |
32 |
||
151 |
100% |
7.820 |
2022-05-27 |
True |
11.146 |
14 |
14 |
14 |
||
152 |
100% |
26.725 |
2022-05-27 |
True |
30.057 |
19 |
19 |
19 |
||
153 |
100% |
54.437 |
2022-05-27 |
True |
57.660 |
24 |
24 |
24 |
||
154 |
100% |
2.995 |
2022-05-27 |
Distance entre deux mots de même longueur et tests unitaires |
True |
6.200 |
17 |
17 |
17 |
|
155 |
100% |
6.824 |
2022-05-27 |
True |
10.098 |
22 |
22 |
22 |
||
156 |
100% |
7.705 |
2022-05-27 |
True |
11.121 |
9 |
9 |
9 |
||
157 |
100% |
8.369 |
2022-05-27 |
True |
11.713 |
15 |
15 |
15 |
||
158 |
100% |
8.516 |
2022-05-27 |
True |
11.927 |
8 |
8 |
8 |
||
159 |
100% |
160.904 |
2022-05-27 |
True |
164.146 |
34 |
34 |
34 |
||
160 |
100% |
136.796 |
2022-05-27 |
True |
139.968 |
15 |
15 |
15 |
||
161 |
100% |
11.939 |
2022-05-27 |
True |
15.244 |
14 |
14 |
14 |
||
162 |
100% |
4.068 |
2022-05-27 |
True |
7.136 |
9 |
9 |
9 |
||
163 |
100% |
19.567 |
2022-05-27 |
True |
22.979 |
38 |
38 |
38 |
||
164 |
100% |
2.832 |
2022-05-27 |
True |
5.990 |
8 |
8 |
8 |
||
165 |
100% |
4.775 |
2022-05-27 |
True |
7.891 |
19 |
19 |
19 |
||
166 |
100% |
0.145 |
2022-05-27 |
True |
3.284 |
8 |
8 |
8 |
||
167 |
0% |
nan |
nan |
146 |
0 |
|||||
168 |
100% |
11.724 |
2022-05-27 |
True |
14.963 |
71 |
71 |
71 |
||
169 |
100% |
0.481 |
2022-05-27 |
True |
3.649 |
5 |
5 |
5 |
||
170 |
0% |
nan |
nan |
72 |
0 |
|||||
171 |
0% |
nan |
nan |
7 |
0 |
|||||
172 |
100% |
1.279 |
2022-05-27 |
True |
4.400 |
8 |
8 |
8 |
||
173 |
0% |
nan |
2A.i - Modèle relationnel, analyse d’incidents dans le transport aérien |
nan |
29 |
0 |
||||
174 |
0% |
nan |
td2a/td2a_cenonce_session_5_donnees_non_structurees_et_programmation_fonctionnelle.ipynb |
2A.i - Données non structurées et programmation fonctionnelle |
nan |
66 |
0 |
|||
175 |
0% |
nan |
nan |
42 |
0 |
|||||
176 |
0% |
nan |
nan |
27 |
0 |
|||||
177 |
0% |
nan |
nan |
9 |
0 |
|||||
178 |
0% |
nan |
nan |
34 |
0 |
|||||
179 |
0% |
nan |
2A.i - Modèle relationnel, analyse d’incidents dans le transport aérien - correction |
nan |
5 |
0 |
||||
180 |
0% |
nan |
td2a/td2a_correction_session_5_donnees_non_structurees_et_programmation_fonctionnelle_corrige.ipynb |
2A.i - Données non structurées, programmation fonctionnelle - correction |
nan |
72 |
0 |
|||
181 |
0% |
nan |
nan |
29 |
0 |
|||||
182 |
0% |
nan |
2A.ml - Classification binaire avec features textuelles - correction |
nan |
39 |
0 |
||||
183 |
100% |
13.318 |
2022-05-27 |
True |
17.013 |
6 |
6 |
6 |
||
184 |
100% |
39.829 |
2022-05-27 |
True |
43.070 |
36 |
36 |
36 |
||
185 |
90% |
116.693 |
2022-05-27 |
True |
120.238 |
42 |
38 |
38 |
||
186 |
100% |
0.142 |
2022-05-27 |
True |
3.319 |
6 |
6 |
6 |
||
187 |
100% |
0.137 |
2022-05-27 |
True |
3.291 |
6 |
6 |
6 |
||
188 |
100% |
6.118 |
2022-05-27 |
True |
9.232 |
8 |
8 |
8 |
||
189 |
100% |
91.735 |
2022-05-27 |
2A.algo - Plus proches voisins en grande dimension - correction |
True |
94.866 |
20 |
20 |
20 |
|
190 |
100% |
0.118 |
2022-05-27 |
True |
3.276 |
6 |
6 |
6 |
||
191 |
100% |
0.172 |
2022-05-27 |
True |
3.265 |
5 |
5 |
5 |
||
192 |
100% |
0.633 |
2022-05-27 |
True |
3.783 |
6 |
6 |
6 |
||
193 |
100% |
0.132 |
2022-05-27 |
True |
3.273 |
6 |
6 |
6 |
||
194 |
0% |
nan |
nan |
62 |
0 |
|||||
195 |
0% |
nan |
nan |
10 |
0 |
|||||
196 |
0% |
nan |
nan |
40 |
0 |
|||||
197 |
0% |
nan |
nan |
61 |
0 |
|||||
198 |
100% |
1.892 |
2022-05-27 |
True |
5.098 |
14 |
14 |
14 |
||
199 |
100% |
6.076 |
2022-05-27 |
True |
9.217 |
15 |
15 |
15 |
||
200 |
100% |
4.566 |
2022-05-27 |
2A.eco - Rappel de ce que vous savez déjà mais avez peut-être oublié |
True |
7.774 |
29 |
29 |
29 |
|
201 |
100% |
15.741 |
2022-05-27 |
td2a_eco/td2a_TD5_Traitement_automatique_des_langues_en_Python.ipynb |
True |
18.960 |
34 |
34 |
34 |
|
202 |
100% |
21.773 |
2022-05-27 |
td2a_eco/td2a_TD5_Traitement_automatique_des_langues_en_Python_correction.ipynb |
2A.eco - Traitement automatique de la langue en Python - correction |
True |
25.131 |
35 |
35 |
35 |
203 |
100% |
0.000 |
2022-05-27 |
True |
3.323 |
1 |
1 |
1 |
||
204 |
100% |
1.140 |
2022-05-27 |
td2a_eco/td2a_eco_exercices_de_manipulation_de_donnees.ipynb |
2A.eco - Mise en pratique des séances 1 et 2 - Utilisation de pandas et visualisation |
True |
4.236 |
3 |
3 |
3 |
205 |
100% |
3.307 |
2022-05-27 |
td2a_eco/td2a_eco_exercices_de_manipulation_de_donnees_correction_a.ipynb |
2A.eco - Mise en pratique des séances 1 et 2 - Utilisation de pandas et visualisation - correction |
True |
6.493 |
10 |
10 |
10 |
206 |
100% |
6.653 |
2022-05-27 |
td2a_eco/td2a_eco_exercices_de_manipulation_de_donnees_correction_b.ipynb |
2A.eco - Mise en pratique des séances 1 et 2 - Utilisation de pandas et visualisation - correction |
True |
9.778 |
16 |
16 |
16 |
207 |
100% |
7.233 |
2022-05-27 |
td2a_eco/td2a_eco_exercices_de_manipulation_de_donnees_correction_c.ipynb |
2A.eco - Mise en pratique des séances 1 et 2 - Utilisation de pandas et visualisation - correction |
True |
10.413 |
14 |
14 |
14 |
208 |
100% |
21.383 |
2022-05-27 |
True |
24.531 |
12 |
12 |
12 |
||
209 |
100% |
7.575 |
2022-05-27 |
True |
11.135 |
26 |
26 |
26 |
||
210 |
100% |
9.430 |
2022-05-27 |
True |
12.838 |
36 |
36 |
36 |
||
211 |
0% |
nan |
nan |
63 |
0 |
|||||
212 |
0% |
nan |
nan |
63 |
0 |
|||||
213 |
0% |
nan |
td2a_eco2/td2a_eco_5d_Travailler_du_texte_les_expressions_regulieres.ipynb |
nan |
26 |
0 |
||||
214 |
0% |
nan |
td2a_eco2/td2a_eco_5d_Travailler_du_texte_les_expressions_regulieres_correction.ipynb |
2A.eco - Les expressions régulières : à quoi ça sert ? (correction) |
nan |
27 |
0 |
|||
215 |
0% |
nan |
td2a_eco2/td2a_eco_NLP_tf_idf_ngrams_LDA_word2vec_sur_des_extraits_litteraires.ipynb |
nan |
115 |
0 |
||||
216 |
100% |
1028.692 |
2022-05-27 |
True |
1032.003 |
37 |
37 |
37 |
||
217 |
0% |
nan |
nan |
48 |
0 |
|||||
218 |
100% |
146.385 |
2022-05-27 |
True |
149.550 |
22 |
22 |
22 |
||
219 |
100% |
13.145 |
2022-05-27 |
True |
16.413 |
41 |
41 |
41 |
||
220 |
100% |
0.326 |
2022-05-27 |
2A.ml - Boosting, random forest, gradient - les features qu’ils aiment |
True |
3.456 |
4 |
4 |
4 |
|
221 |
100% |
6.812 |
2022-05-27 |
True |
9.996 |
27 |
27 |
27 |
||
222 |
100% |
98.687 |
2022-05-27 |
Hyperparamètres, LassoRandomForestRregressor et grid_search (correction) |
True |
101.838 |
15 |
15 |
15 |
|
223 |
100% |
5.079 |
2022-05-27 |
Hyperparamètres, LassoRandomForestRregressor et grid_search (énoncé) |
True |
8.218 |
4 |
4 |
4 |
|
224 |
100% |
3.082 |
2022-05-27 |
True |
6.262 |
12 |
12 |
12 |
||
225 |
100% |
12.442 |
2022-05-27 |
Rappels sur scikit-learn et le machine learning (correction) |
True |
15.558 |
21 |
21 |
21 |
|
226 |
100% |
20.239 |
2022-05-27 |
True |
23.458 |
42 |
42 |
42 |
||
227 |
0% |
nan |
nan |
36 |
0 |
|||||
228 |
0% |
nan |
nan |
31 |
0 |
|||||
229 |
100% |
4.179 |
2022-05-27 |
True |
7.322 |
9 |
9 |
9 |
||
230 |
100% |
4.411 |
2022-05-27 |
True |
7.535 |
6 |
6 |
6 |
||
231 |
100% |
40.999 |
2022-05-27 |
True |
44.204 |
25 |
25 |
25 |
||
232 |
100% |
45.953 |
2022-05-27 |
2A.data - Classification, régression, anomalies - correction |
True |
49.305 |
33 |
33 |
33 |
|
233 |
0% |
nan |
2A.ml - Statistiques descriptives avec scikit-learn - correction |
nan |
37 |
0 |
||||
234 |
0% |
nan |
nan |
46 |
0 |
|||||
235 |
100% |
28.991 |
2022-05-27 |
True |
32.162 |
26 |
26 |
26 |
||
236 |
100% |
5.671 |
2022-05-27 |
True |
9.096 |
9 |
9 |
9 |
||
237 |
0% |
nan |
nan |
42 |
0 |
|||||
238 |
0% |
nan |
nan |
6 |
0 |
|||||
239 |
100% |
25.908 |
2022-05-27 |
2A.ml - Pipeline pour un réduction d’une forêt aléatoire - correction |
True |
29.043 |
24 |
24 |
24 |
|
240 |
100% |
5.662 |
2022-05-27 |
2A.ml - Pipeline pour un réduction d’une forêt aléatoire - énoncé |
True |
8.733 |
11 |
11 |
11 |
|
241 |
100% |
3.647 |
2022-05-27 |
True |
6.819 |
6 |
6 |
6 |
||
242 |
100% |
36.198 |
2022-05-27 |
True |
39.351 |
44 |
44 |
44 |
||
243 |
100% |
5.694 |
2022-05-27 |
True |
8.786 |
15 |
15 |
15 |
||
244 |
100% |
19.896 |
2022-05-27 |
True |
23.085 |
15 |
15 |
15 |
||
245 |
100% |
147.536 |
2022-05-27 |
True |
150.799 |
19 |
19 |
19 |
||
246 |
100% |
4.118 |
2022-05-27 |
True |
7.581 |
8 |
8 |
8 |
