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| = Results for Exercise Sheet 10 (Naive Bayes) = | = Results for Exercise Sheet 11 (Logistic Regression) = |
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| In the first column, write down your account name, or name1+name2 if you work in a group.<<BR>> In the columns 2-5, write down the average value of the F-measure for each of the four sets of train and test data provided on the Wiki (with '''exactly two digits of precision'''). <<BR>> In the 6th column, give a concise description about the refinements you have implemented.<<BR>> ||<tablewidth=""" tableheight=""" tablestyle="100%; auto; "style="text-align:center;">'''Name''' ||'''Genres''', ''Variant 1'' ||'''Genres''', ''Variant 2'' ||'''Ratings''', ''Variant 1'' ||'''Ratings''', ''Variant 2'' ||'''Implemented Refinements''' || ||pb1042 ||F = 77.49% ||F = 78.46% ||F = 47.47% ||F = 47.48% ||None || |
In the first column, write down your account name, or name1+name2 if you work in a group.<<BR>> In the columns 2-4, write down the precision, recall and F-measure for the class '''Western'''. In columns 5-7, write down the precision, recall and F-measure for the class '''R'''. <<BR>> In columns '''alpha''', '''#epochs''' and '''batch size''' please write down your learning rate alpha, the number of epochs you used for training, and your batch size. Please note in the last column which datasets you used (Variant 1 or Variant 2).<<BR>> ||<tablewidth=""" tableheight=""" tablestyle="" & quot; & amp; quot; 100%& amp; amp; quot; ; auto& amp; amp; quot& amp; quot; ; & amp; amp; quot& amp; quot; ; & amp; amp; quot& amp; quot& quot; ; & amp; quot& quot; ; & amp; quot& quot" ;&quot";&quot""style="" & quot; & amp; quot; & amp; amp; quot;text-align:center& amp; amp; quot; & amp; quot; & quot; "" |2>'''Name''' ||||||<style="" & quot; & amp; quot;text-align:center& amp; quot; & quot; "">'''Class: Western''' ||||||<style="" & quot; & amp; quot;text-align:center& amp; quot; & quot; "">'''Class: R''' |||||||||| ||'''Precision''' ||'''Recall''' ||'''F-Measure''' ||'''Precision''' ||'''Recall''' ||'''F-Measure''' || '''alpha''' || '''#epochs''' || '''batch size''' ||'''Dataset variant'''|| ||pb1042 || 90.62% || 84.94% || 87.69% || 71.20% || 77.16% || 74.06% || 0.25 || 20 || 10 || Variant 1 || ||ta116 || 99.52% || 96.12% || 97.79% || 69.81% || 86.90% || 77.42% || 0.5 || 50 || 20 || Variant 1|| ||sw540 || 92.52% || 82.85% || 87.42% || 67.07%% || 93.43% || 78.09% || 0.3 || 20 || 64 || Variant 1|| ||ak482 || 91.35% || 92.23% || 91.79% || 41.6% || 61.86% || 50.07% || 1 || 10 || 5|| Variant 2|| ||mb925 || 94.00% || 91.26% || 92.61% || 45.97% || 59.50% || 51.87% || 0.2 || 40 || 10 || Variant 2|| ||tl109 || 79.63% || 70.12% || 73.96% || 45.08% || 44.65% || 44.59% || 0.5 || 50 || 20 || Variant 1|| ||ds565 || 90.99% || 84.52% || 87.64% || 71.30% || 77.16% || 74.11% || 0.2 || 20 || 10 || Variant 1 || ||jr74+tw246 || 89.29% || 83.68% || 86.39% || 72.10% || 71.58% || 71.84% || 0.25 || 20 || 10 || Variant 1 || ||wy8+zl36 || 92.42% || 81.59% || 86.67% || 68.41% || 89.88% || 77.69% || 0.25 || 10 || 20 || Variant 1 || ||hd103 + sa285 || 94.03% || 91.75% || 92.87% || 46.24% || 58.82% || 51.78% || 0.25 || 50 || 10 || Variant 2 || ||rt53 || 90.62% || 84.94% || 87.69% || 71.20% || 77.16% || 74.06% || 0.25 || 10 || 20 || Variant 1 || ||bw63 || 77.73% || 83.01% || 80.28% || 44.37% || 44.01% || 46.38% || 0.25 || 10 || 50 || Variant 2 || ||sr463|| 88.40% || 82.15% || 85.64% || 69.84% || 76.28% || 73.23% || 0.25 || 20 || 10 || Variant 2 || |
Results for Exercise Sheet 11 (Logistic Regression)
Click "Edit (Text)" in the upper bar of this page and add your row to the table below, following the examples already there.
In the first column, write down your account name, or name1+name2 if you work in a group.
In the columns 2-4, write down the precision, recall and F-measure for the class Western. In columns 5-7, write down the precision, recall and F-measure for the class R.
In columns alpha, #epochs and batch size please write down your learning rate alpha, the number of epochs you used for training, and your batch size. Please note in the last column which datasets you used (Variant 1 or Variant 2).
Name |
Class: Western |
Class: R |
||||||||
Precision |
Recall |
F-Measure |
Precision |
Recall |
F-Measure |
alpha |
#epochs |
batch size |
Dataset variant |
|
pb1042 |
90.62% |
84.94% |
87.69% |
71.20% |
77.16% |
74.06% |
0.25 |
20 |
10 |
Variant 1 |
ta116 |
99.52% |
96.12% |
97.79% |
69.81% |
86.90% |
77.42% |
0.5 |
50 |
20 |
Variant 1 |
sw540 |
92.52% |
82.85% |
87.42% |
67.07%% |
93.43% |
78.09% |
0.3 |
20 |
64 |
Variant 1 |
ak482 |
91.35% |
92.23% |
91.79% |
41.6% |
61.86% |
50.07% |
1 |
10 |
5 |
Variant 2 |
mb925 |
94.00% |
91.26% |
92.61% |
45.97% |
59.50% |
51.87% |
0.2 |
40 |
10 |
Variant 2 |
tl109 |
79.63% |
70.12% |
73.96% |
45.08% |
44.65% |
44.59% |
0.5 |
50 |
20 |
Variant 1 |
ds565 |
90.99% |
84.52% |
87.64% |
71.30% |
77.16% |
74.11% |
0.2 |
20 |
10 |
Variant 1 |
jr74+tw246 |
89.29% |
83.68% |
86.39% |
72.10% |
71.58% |
71.84% |
0.25 |
20 |
10 |
Variant 1 |
wy8+zl36 |
92.42% |
81.59% |
86.67% |
68.41% |
89.88% |
77.69% |
0.25 |
10 |
20 |
Variant 1 |
hd103 + sa285 |
94.03% |
91.75% |
92.87% |
46.24% |
58.82% |
51.78% |
0.25 |
50 |
10 |
Variant 2 |
rt53 |
90.62% |
84.94% |
87.69% |
71.20% |
77.16% |
74.06% |
0.25 |
10 |
20 |
Variant 1 |
bw63 |
77.73% |
83.01% |
80.28% |
44.37% |
44.01% |
46.38% |
0.25 |
10 |
50 |
Variant 2 |
sr463 |
88.40% |
82.15% |
85.64% |
69.84% |
76.28% |
73.23% |
0.25 |
20 |
10 |
Variant 2 |