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| ||<tablewidth="200em">'''Name''' ||'''Reading time''' ||'''Training time'''|| '''Prediction time'''||'''Correct %''' ||'''Processor / RAM''' ||'''Language''' ||'''Feature Selection''' || | ||<tablewidth="100em">'''Name''' ||'''Reading time''' ||'''Training time'''|| '''Prediction time'''||'''Correct %''' ||'''Processor / RAM''' ||'''Language''' ||'''Feature Selection''' || |
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| ||Matthias F ||10.0s ||1.0s ||8.3.2s ||37% || Intel i7-3612QM CPU @ 2.10GHz / 8GB ||C++ || || | ||Matthias F ||10.0s ||1.0s ||8.3.2s ||37% || Intel i7 CPU @ 2.10GHz / 8GB ||C++ || || |
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| ||Markus F ||8.6s ||0.02s ||5.2s ||54% || Intel Core i5-2500K @ 3.30GHz / 8GB ||C++ || no stopwords || ||Markus F ||8.6s ||0.02s ||10.7s ||75% || Intel Core i5-2500K @ 3.30GHz / 8GB ||C++ || no stopwords, 10% of all documents as training set || |
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. 1653 training points. Used docs with multiple class labels (counting them). |
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 |
0.1s |
0.1s |
47% |
Intel i7 @ 3.4GHz / 16GB |
C++ |
|
Adrian B. |
3.0s |
0.1s |
0.1s |
55% |
Intel i7 @ 3.4GHz / 16GB |
C++ |
no stopwords |
Adrian B. |
3.0s |
0.1s |
0.1s |
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 |
0.1s |
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 |
1.0s |
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 CPU @ 2.10GHz / 8GB |
C++ |
|
Andreas H |
3.8s |
0.03s |
1.9s |
50% |
Intel P8700 @ 2.53GHz / 4GB |
C++ |
word length >= 9 |
Markus F |
8.6s |
0.02s |
5.2s |
54% |
Intel Core i5-2500K @ 3.30GHz / 8GB |
C++ |
no stopwords |
Markus F |
8.6s |
0.02s |
10.7s |
75% |
Intel Core i5-2500K @ 3.30GHz / 8GB |
C++ |
no stopwords, 10% of all documents as training set |
