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Size: 1213
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Size: 3154
Comment:
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| Deletions are marked like this. | Additions are marked like this. |
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| ||Florian B ||6.5s ||16.0s ||9.5s ||30%|| Intel X5560 @ 2.8GHz / 36GB ||Java || || ||Florian B ||xs ||xs ||xs ||55%|| Intel X5560 @ 2.8GHz / 36GB ||Java ||no stopwords + only frequent words || ||Christoph S ||15.5s ||0.4s ||0.7s ||53%|| Core 2 Quad @ 2.8GHz / 4GB ||C++ || || |
||Florian B ||7.3s ||14.9s ||9.5s ||49% || Intel X5560 @ 2.8GHz / 36GB ||Java || || ||Florian B ||7.9s ||15.0s ||8.6s ||53% || Intel X5560 @ 2.8GHz / 36GB ||Java ||no stopwords || ||Christoph S ||15.5s ||0.4s ||0.7s ||53% || Core 2 Quad @ 2.8GHz / 4GB ||C++ || || ||Christoph S ||36.6s ||1.1s ||4.2s ||62% || Core 2 Duo @ 1.6GHz / 2GB ||C++ ||no stopwords || ||Christoph S ||34.3s ||0.0s ||0.0s ||21% || Core 2 Duo @ 1.6GHz / 2GB ||C++ ||Just predict "class 0" all the time ;) || ||Christoph S ||34.3s ||1.1s ||4.1s ||67% || Core 2 Duo @ 1.6GHz / 2GB ||C++ ||No stopwords. Use 1653 training points. Documents with multiple class labels are used but counted as often as they are used. || ||Matthias H. ||24.8s ||0.4s ||1.7s ||42% || Intel i3 @ 1.3GHz / 4GB ||Java || || ||Stefan ||10.0s ||0.1s ||43.0s ||47% || Intel i5 @ 1.7GHz / 4GB ||Java || || ||Anthony ||7.0s ||6.0s ||23.0s ||18% || Intel i7 @ 2.10GHz / 8GB ||Java || || ||Adrian B. ||3.5s ||0.05s ||0.1s ||47% || Intel i7 @ 3.4GHz / 16GB ||C++ || || ||Adrian B. ||3.0s ||0.05s ||0.06s ||55% || Intel i7 @ 3.4GHz / 16GB ||C++ ||no stopwords || ||Adrian B. ||3.0s ||0.05s ||0.06s ||67% || Intel i7 @ 3.4GHz / 16GB ||C++ ||no stopwords. Use every tenth doc for training, no matter how many labels it has. || ||Aritz B ||7.7s ||0.1s ||5.4s ||26% || Intel Core 2 Duo @ 2.4GHz / 6GB ||Java || || ||Tobias S|| 23.9s || 1.1s ||46.9s ||22% || Intel Atom @ 1.6GHz / 2GB || C++|| || ||Chris Sch|| 17.8s || 0.2s ||16.0s ||37% || Core 2 Duo @ 2.5GHz / 4GB || Java|| || ||Simon S||18.8s ||0.07s ||8.9s ||27% || Intel X5560 @ 2.8GHz / 36GB ||C++ || || ||Ander B ||8.4s ||1.6s ||12.8s ||26% || Intel Core i5 @ 2.3GHz / 8GB ||Java || || ||Ane R ||7.6s ||1s ||4.6s ||27% || Intel i5-2450M @ 2.5GHz / 8GB ||Java || || ||Alves J ||7.9s ||4.2s ||17.1s ||29% || Intel Core 2 Duo @ 2.4GHz / 4GB ||Java || || ||Tobias F ||10,3s ||3,4s ||24,8s ||19% || Phenom II X4 965 @ 3.4GHz / 4GB ||Java || || ||Jan M ||21.3s ||0.3s ||1.5s ||54% || Intel E5645 @ 2.4GHz / 23GB ||C++ || || ||Jan M ||18.2s ||0.3s ||1.2s ||62% || Intel E5645 @ 2.4GHz / 23GB ||C++ ||no stopwords || ||Matthias F ||10.0s ||1.0s ||8.3.2s ||37% || Intel i7-3612QM CPU @ 2.10GHz / 8GB ||C++ || || |
Results for Exercise Sheet 10 (Naive Bayes)
Please read the instructions below, before adding something to the table!
Add your row to the table below, following the examples already there. Column 2, 3, and 4 = running time of your program for reading the CSV file, training, and prediction, respectively, in seconds (with exactly one digit after the dot). Column 5 = percentage of your predictions that were correct. Column 5 = machine specification as usual (processor frequency with exactly one digit after the dot, amount of RAM as an integer, no secondary details about processor). Column 6 = programming language (Java or C++). Column 7 = A short description of the feature selection improvements you made (if you made any).
Name |
Reading time |
Training time |
Prediction time |
Correct % |
Processor / RAM |
Language |
Feature Selection |
Florian B |
7.3s |
14.9s |
9.5s |
49% |
Intel X5560 @ 2.8GHz / 36GB |
Java |
|
Florian B |
7.9s |
15.0s |
8.6s |
53% |
Intel X5560 @ 2.8GHz / 36GB |
Java |
no stopwords |
Christoph S |
15.5s |
0.4s |
0.7s |
53% |
Core 2 Quad @ 2.8GHz / 4GB |
C++ |
|
Christoph S |
36.6s |
1.1s |
4.2s |
62% |
Core 2 Duo @ 1.6GHz / 2GB |
C++ |
no stopwords |
Christoph S |
34.3s |
0.0s |
0.0s |
21% |
Core 2 Duo @ 1.6GHz / 2GB |
C++ |
Just predict "class 0" all the time |
Christoph S |
34.3s |
1.1s |
4.1s |
67% |
Core 2 Duo @ 1.6GHz / 2GB |
C++ |
No stopwords. Use 1653 training points. Documents with multiple class labels are used but counted as often as they are used. |
Matthias H. |
24.8s |
0.4s |
1.7s |
42% |
Intel i3 @ 1.3GHz / 4GB |
Java |
|
Stefan |
10.0s |
0.1s |
43.0s |
47% |
Intel i5 @ 1.7GHz / 4GB |
Java |
|
Anthony |
7.0s |
6.0s |
23.0s |
18% |
Intel i7 @ 2.10GHz / 8GB |
Java |
|
Adrian B. |
3.5s |
0.05s |
0.1s |
47% |
Intel i7 @ 3.4GHz / 16GB |
C++ |
|
Adrian B. |
3.0s |
0.05s |
0.06s |
55% |
Intel i7 @ 3.4GHz / 16GB |
C++ |
no stopwords |
Adrian B. |
3.0s |
0.05s |
0.06s |
67% |
Intel i7 @ 3.4GHz / 16GB |
C++ |
no stopwords. Use every tenth doc for training, no matter how many labels it has. |
Aritz B |
7.7s |
0.1s |
5.4s |
26% |
Intel Core 2 Duo @ 2.4GHz / 6GB |
Java |
|
Tobias S |
23.9s |
1.1s |
46.9s |
22% |
Intel Atom @ 1.6GHz / 2GB |
C++ |
|
Chris Sch |
17.8s |
0.2s |
16.0s |
37% |
Core 2 Duo @ 2.5GHz / 4GB |
Java |
|
Simon S |
18.8s |
0.07s |
8.9s |
27% |
Intel X5560 @ 2.8GHz / 36GB |
C++ |
|
Ander B |
8.4s |
1.6s |
12.8s |
26% |
Intel Core i5 @ 2.3GHz / 8GB |
Java |
|
Ane R |
7.6s |
1s |
4.6s |
27% |
Intel i5-2450M @ 2.5GHz / 8GB |
Java |
|
Alves J |
7.9s |
4.2s |
17.1s |
29% |
Intel Core 2 Duo @ 2.4GHz / 4GB |
Java |
|
Tobias F |
10,3s |
3,4s |
24,8s |
19% |
Phenom II X4 965 @ 3.4GHz / 4GB |
Java |
|
Jan M |
21.3s |
0.3s |
1.5s |
54% |
Intel E5645 @ 2.4GHz / 23GB |
C++ |
|
Jan M |
18.2s |
0.3s |
1.2s |
62% |
Intel E5645 @ 2.4GHz / 23GB |
C++ |
no stopwords |
Matthias F |
10.0s |
1.0s |
8.3.2s |
37% |
Intel i7-3612QM CPU @ 2.10GHz / 8GB |
C++ |
|
