Finished 10 runs for k-Nearest Neighbors(3) algorithm
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Finished 10 runs for Support Vector Machine with Linear Kernel algorithm
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Finished 10 runs for Support Vector Machine with RBF Kernel algorithm
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Examining the result
Checking the dataframe of F1-scores
d1
k-Nearest Neighbors(3)
Support Vector Machine with Linear Kernel
Support Vector Machine with RBF Kernel
0
0.944724
0.861314
0.000000
1
0.968215
0.855746
0.641766
2
0.946292
0.863850
0.289474
3
0.967742
0.856481
0.000000
4
0.969849
0.865116
0.020202
5
0.966234
0.875912
0.000000
6
0.967581
0.868545
0.000000
7
0.957179
0.852381
0.000000
8
0.962791
0.871194
0.000000
9
0.948980
0.897959
0.010204
Stats of the d1 to compare algorithms
d1.describe().T
count
mean
std
min
25%
50%
75%
max
k-Nearest Neighbors(3)
10.0
0.959959
0.009884
0.944724
0.951029
0.964512
0.967702
0.969849
Support Vector Machine with Linear Kernel
10.0
0.866850
0.013142
0.852381
0.857690
0.864483
0.870532
0.897959
Support Vector Machine with RBF Kernel
10.0
0.096165
0.211790
0.000000
0.000000
0.000000
0.017703
0.641766
Checking the dataframe of fitting/training time
d2
k-Nearest Neighbors(3)
Support Vector Machine with Linear Kernel
Support Vector Machine with RBF Kernel
0
0.979
43.942
124.595
1
0.000
45.477
137.699
2
0.000
45.893
107.373
3
0.981
49.376
141.589
4
0.000
44.429
148.940
5
0.976
43.940
147.467
6
0.977
42.083
134.906
7
0.974
46.962
139.639
8
0.000
42.484
130.852
9
0.976
61.050
113.273
d2.describe().T
count
mean
std
min
25%
50%
75%
max
k-Nearest Neighbors(3)
10.0
0.5863
0.504610
0.000
0.00000
0.9750
0.97675
0.981
Support Vector Machine with Linear Kernel
10.0
46.5636
5.523546
42.083
43.94050
44.9530
46.69475
61.050
Support Vector Machine with RBF Kernel
10.0
132.6333
13.851391
107.373
126.15925
136.3025
141.10150
148.940
Visualizing the results with the plot_bars function
Make sure to pass the correct titles of the plots. Otherwise, default strings will be plotted which may indicate wrong thing for your experiment.
plot_bars(d1,t1="Mean F1 score of algorithms",
t2="Std.dev of the F1 scores of algorithms")
plot_bars(d2,t1="Mean fitting time of algorithms",
t2="Std.dev of the fitting time of algorithms")