Data
GTSRB-HueHist

GTSRB-HueHist

active ARFF The data is free to use (when citing the publication). Visibility: public Uploaded 24-07-2019 by Matthias Feurer
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The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. # Pre-calculated features To allow scientists without a background in image processing to participate, we several provide pre-calculated feature sets. Each feature set contains the same directory structure as the training image set. For details on the parameters of the feature algorithm, please have a look at the file Feature_description.txt which is part of each archive file. \# Hue Histograms For each image in the training set, the file contains a 256-bin histogram of hue values (HSV color space).

257 features

class (target)nominal43 unique values
0 missing
pixel-00000numeric2817 unique values
0 missing
pixel-00001numeric8639 unique values
0 missing
pixel-00002numeric9637 unique values
0 missing
pixel-00003numeric9817 unique values
0 missing
pixel-00004numeric9783 unique values
0 missing
pixel-00005numeric9460 unique values
0 missing
pixel-00006numeric9183 unique values
0 missing
pixel-00007numeric9087 unique values
0 missing
pixel-00008numeric8285 unique values
0 missing
pixel-00009numeric9456 unique values
0 missing
pixel-00010numeric7601 unique values
0 missing
pixel-00011numeric8972 unique values
0 missing
pixel-00012numeric8015 unique values
0 missing
pixel-00013numeric7754 unique values
0 missing
pixel-00014numeric8928 unique values
0 missing
pixel-00015numeric7297 unique values
0 missing
pixel-00016numeric7349 unique values
0 missing
pixel-00017numeric7786 unique values
0 missing
pixel-00018numeric7621 unique values
0 missing
pixel-00019numeric7327 unique values
0 missing
pixel-00020numeric6803 unique values
0 missing
pixel-00021numeric9313 unique values
0 missing
pixel-00022numeric5628 unique values
0 missing
pixel-00023numeric6781 unique values
0 missing
pixel-00024numeric7096 unique values
0 missing
pixel-00025numeric5924 unique values
0 missing
pixel-00026numeric7327 unique values
0 missing
pixel-00027numeric6692 unique values
0 missing
pixel-00028numeric7829 unique values
0 missing
pixel-00029numeric5532 unique values
0 missing
pixel-00030numeric6376 unique values
0 missing
pixel-00031numeric5645 unique values
0 missing
pixel-00032numeric6576 unique values
0 missing
pixel-00033numeric5652 unique values
0 missing
pixel-00034numeric5907 unique values
0 missing
pixel-00035numeric5890 unique values
0 missing
pixel-00036numeric5300 unique values
0 missing
pixel-00037numeric4775 unique values
0 missing
pixel-00038numeric4953 unique values
0 missing
pixel-00039numeric4711 unique values
0 missing
pixel-00040numeric4064 unique values
0 missing
pixel-00041numeric3299 unique values
0 missing
pixel-00042numeric1600 unique values
0 missing
pixel-00043numeric11046 unique values
0 missing
pixel-00044numeric2757 unique values
0 missing
pixel-00045numeric3313 unique values
0 missing
pixel-00046numeric3745 unique values
0 missing
pixel-00047numeric3818 unique values
0 missing
pixel-00048numeric3614 unique values
0 missing
pixel-00049numeric3836 unique values
0 missing
pixel-00050numeric4176 unique values
0 missing
pixel-00051numeric4190 unique values
0 missing
pixel-00052numeric3385 unique values
0 missing
pixel-00053numeric4532 unique values
0 missing
pixel-00054numeric2970 unique values
0 missing
pixel-00055numeric3703 unique values
0 missing
pixel-00056numeric2505 unique values
0 missing
pixel-00057numeric4961 unique values
0 missing
pixel-00058numeric3409 unique values
0 missing
pixel-00059numeric2193 unique values
0 missing
pixel-00060numeric3962 unique values
0 missing
pixel-00061numeric3433 unique values
0 missing
pixel-00062numeric2582 unique values
0 missing
pixel-00063numeric1811 unique values
0 missing
pixel-00064numeric5694 unique values
0 missing
pixel-00065numeric2224 unique values
0 missing
pixel-00066numeric2689 unique values
0 missing
pixel-00067numeric2880 unique values
0 missing
pixel-00068numeric3356 unique values
0 missing
pixel-00069numeric2526 unique values
0 missing
pixel-00070numeric2141 unique values
0 missing
pixel-00071numeric4333 unique values
0 missing
pixel-00072numeric2131 unique values
0 missing
pixel-00073numeric2680 unique values
0 missing
pixel-00074numeric3617 unique values
0 missing
pixel-00075numeric1481 unique values
0 missing
pixel-00076numeric2008 unique values
0 missing
pixel-00077numeric3153 unique values
0 missing
pixel-00078numeric2709 unique values
0 missing
pixel-00079numeric2273 unique values
0 missing
pixel-00080numeric2308 unique values
0 missing
pixel-00081numeric2037 unique values
0 missing
pixel-00082numeric1531 unique values
0 missing
pixel-00083numeric1242 unique values
0 missing
pixel-00084numeric466 unique values
0 missing
pixel-00085numeric6567 unique values
0 missing
pixel-00086numeric461 unique values
0 missing
pixel-00087numeric1097 unique values
0 missing
pixel-00088numeric1562 unique values
0 missing
pixel-00089numeric1986 unique values
0 missing
pixel-00090numeric2270 unique values
0 missing
pixel-00091numeric2185 unique values
0 missing
pixel-00092numeric2507 unique values
0 missing
pixel-00093numeric1571 unique values
0 missing
pixel-00094numeric3136 unique values
0 missing
pixel-00095numeric1398 unique values
0 missing
pixel-00096numeric3448 unique values
0 missing
pixel-00097numeric2475 unique values
0 missing
pixel-00098numeric2088 unique values
0 missing
pixel-00099numeric4121 unique values
0 missing
pixel-00100numeric1923 unique values
0 missing
pixel-00101numeric2309 unique values
0 missing
pixel-00102numeric3099 unique values
0 missing
pixel-00103numeric2604 unique values
0 missing
pixel-00104numeric2380 unique values
0 missing
pixel-00105numeric1891 unique values
0 missing
pixel-00106numeric5281 unique values
0 missing
pixel-00107numeric1336 unique values
0 missing
pixel-00108numeric2308 unique values
0 missing
pixel-00109numeric2943 unique values
0 missing
pixel-00110numeric1906 unique values
0 missing
pixel-00111numeric3426 unique values
0 missing
pixel-00112numeric2817 unique values
0 missing
pixel-00113numeric4575 unique values
0 missing
pixel-00114numeric2059 unique values
0 missing
pixel-00115numeric3116 unique values
0 missing
pixel-00116numeric2407 unique values
0 missing
pixel-00117numeric3961 unique values
0 missing
pixel-00118numeric2856 unique values
0 missing
pixel-00119numeric3675 unique values
0 missing
pixel-00120numeric3545 unique values
0 missing
pixel-00121numeric3185 unique values
0 missing
pixel-00122numeric2916 unique values
0 missing
pixel-00123numeric3146 unique values
0 missing
pixel-00124numeric3122 unique values
0 missing
pixel-00125numeric2755 unique values
0 missing
pixel-00126numeric2206 unique values
0 missing
pixel-00127numeric876 unique values
0 missing
pixel-00128numeric10019 unique values
0 missing
pixel-00129numeric2809 unique values
0 missing
pixel-00130numeric3447 unique values
0 missing
pixel-00131numeric3954 unique values
0 missing
pixel-00132numeric4110 unique values
0 missing
pixel-00133numeric4200 unique values
0 missing
pixel-00134numeric4506 unique values
0 missing
pixel-00135numeric5025 unique values
0 missing
pixel-00136numeric5159 unique values
0 missing
pixel-00137numeric4720 unique values
0 missing
pixel-00138numeric5816 unique values
0 missing
pixel-00139numeric4795 unique values
0 missing
pixel-00140numeric5596 unique values
0 missing
pixel-00141numeric4825 unique values
0 missing
pixel-00142numeric6831 unique values
0 missing
pixel-00143numeric6154 unique values
0 missing
pixel-00144numeric5204 unique values
0 missing
pixel-00145numeric6957 unique values
0 missing
pixel-00146numeric6899 unique values
0 missing
pixel-00147numeric6464 unique values
0 missing
pixel-00148numeric6208 unique values
0 missing
pixel-00149numeric9009 unique values
0 missing
pixel-00150numeric7352 unique values
0 missing
pixel-00151numeric8165 unique values
0 missing
pixel-00152numeric8722 unique values
0 missing
pixel-00153numeric9540 unique values
0 missing
pixel-00154numeric9551 unique values
0 missing
pixel-00155numeric9454 unique values
0 missing
pixel-00156numeric10752 unique values
0 missing
pixel-00157numeric9648 unique values
0 missing
pixel-00158numeric10053 unique values
0 missing
pixel-00159numeric10187 unique values
0 missing
pixel-00160numeric7874 unique values
0 missing
pixel-00161numeric8073 unique values
0 missing
pixel-00162numeric9236 unique values
0 missing
pixel-00163numeric8493 unique values
0 missing
pixel-00164numeric7571 unique values
0 missing
pixel-00165numeric7423 unique values
0 missing
pixel-00166numeric6919 unique values
0 missing
pixel-00167numeric5760 unique values
0 missing
pixel-00168numeric5097 unique values
0 missing
pixel-00169numeric3125 unique values
0 missing
pixel-00170numeric13628 unique values
0 missing
pixel-00171numeric2805 unique values
0 missing
pixel-00172numeric4113 unique values
0 missing
pixel-00173numeric4791 unique values
0 missing
pixel-00174numeric5350 unique values
0 missing
pixel-00175numeric5529 unique values
0 missing
pixel-00176numeric5374 unique values
0 missing
pixel-00177numeric5836 unique values
0 missing
pixel-00178numeric4154 unique values
0 missing
pixel-00179numeric6986 unique values
0 missing
pixel-00180numeric3635 unique values
0 missing
pixel-00181numeric7518 unique values
0 missing
pixel-00182numeric5169 unique values
0 missing
pixel-00183numeric4435 unique values
0 missing
pixel-00184numeric8270 unique values
0 missing
pixel-00185numeric3978 unique values
0 missing
pixel-00186numeric4492 unique values
0 missing
pixel-00187numeric5735 unique values
0 missing
pixel-00188numeric4880 unique values
0 missing
pixel-00189numeric4328 unique values
0 missing
pixel-00190numeric3562 unique values
0 missing
pixel-00191numeric9074 unique values
0 missing
pixel-00192numeric2382 unique values
0 missing
pixel-00193numeric3859 unique values
0 missing
pixel-00194numeric4835 unique values
0 missing
pixel-00195numeric3130 unique values
0 missing
pixel-00196numeric5458 unique values
0 missing
pixel-00197numeric4323 unique values
0 missing
pixel-00198numeric7319 unique values
0 missing
pixel-00199numeric2917 unique values
0 missing
pixel-00200numeric4443 unique values
0 missing
pixel-00201numeric3258 unique values
0 missing
pixel-00202numeric5756 unique values
0 missing
pixel-00203numeric3787 unique values
0 missing
pixel-00204numeric4894 unique values
0 missing
pixel-00205numeric4624 unique values
0 missing
pixel-00206numeric3956 unique values
0 missing
pixel-00207numeric3478 unique values
0 missing
pixel-00208numeric3749 unique values
0 missing
pixel-00209numeric3509 unique values
0 missing
pixel-00210numeric2702 unique values
0 missing
pixel-00211numeric1820 unique values
0 missing
pixel-00212numeric531 unique values
0 missing
pixel-00213numeric10446 unique values
0 missing
pixel-00214numeric1878 unique values
0 missing
pixel-00215numeric2857 unique values
0 missing
pixel-00216numeric3639 unique values
0 missing
pixel-00217numeric3800 unique values
0 missing
pixel-00218numeric3825 unique values
0 missing
pixel-00219numeric4229 unique values
0 missing
pixel-00220numeric4914 unique values
0 missing
pixel-00221numeric5188 unique values
0 missing
pixel-00222numeric4052 unique values
0 missing
pixel-00223numeric6047 unique values
0 missing
pixel-00224numeric3738 unique values
0 missing
pixel-00225numeric4910 unique values
0 missing
pixel-00226numeric3445 unique values
0 missing
pixel-00227numeric7152 unique values
0 missing
pixel-00228numeric5151 unique values
0 missing
pixel-00229numeric3447 unique values
0 missing
pixel-00230numeric6241 unique values
0 missing
pixel-00231numeric5583 unique values
0 missing
pixel-00232numeric4640 unique values
0 missing
pixel-00233numeric3626 unique values
0 missing
pixel-00234numeric8772 unique values
0 missing
pixel-00235numeric4759 unique values
0 missing
pixel-00236numeric5600 unique values
0 missing
pixel-00237numeric5897 unique values
0 missing
pixel-00238numeric6752 unique values
0 missing
pixel-00239numeric5803 unique values
0 missing
pixel-00240numeric5665 unique values
0 missing
pixel-00241numeric7983 unique values
0 missing
pixel-00242numeric6086 unique values
0 missing
pixel-00243numeric6971 unique values
0 missing
pixel-00244numeric7696 unique values
0 missing
pixel-00245numeric5929 unique values
0 missing
pixel-00246numeric6579 unique values
0 missing
pixel-00247numeric8106 unique values
0 missing
pixel-00248numeric7948 unique values
0 missing
pixel-00249numeric7688 unique values
0 missing
pixel-00250numeric7924 unique values
0 missing
pixel-00251numeric8164 unique values
0 missing
pixel-00252numeric7939 unique values
0 missing
pixel-00253numeric8232 unique values
0 missing
pixel-00254numeric7231 unique values
0 missing
pixel-00255numeric15760 unique values
0 missing

62 properties

51839
Number of instances (rows) of the dataset.
257
Number of attributes (columns) of the dataset.
43
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
256
Number of numeric attributes.
1
Number of nominal attributes.
3.74
First quartile of skewness among attributes of the numeric type.
0
Mean of means among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.77
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
5.02
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
40.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
43
Average number of distinct values among the attributes of the nominal type.
0
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
5.79
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
5.79
Percentage of instances belonging to the most frequent class.
0.01
Mean standard deviation of attributes of the numeric type.
4.73
Second quartile (Median) of skewness among attributes of the numeric type.
3000
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
5.75
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1555.3
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
85.58
Third quartile of kurtosis among attributes of the numeric type.
0.08
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.61
Percentage of numeric attributes.
0.01
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
43
The minimal number of distinct values among attributes of the nominal type.
0.39
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
43
The maximum number of distinct values among attributes of the nominal type.
2.01
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
6.33
Third quartile of skewness among attributes of the numeric type.
25.67
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
23.52
First quartile of kurtosis among attributes of the numeric type.
0.01
Third quartile of standard deviation of attributes of the numeric type.
0.09
Maximum standard deviation of attributes of the numeric type.
0.52
Percentage of instances belonging to the least frequent class.
270
Number of instances belonging to the least frequent class.
0
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
97.8
Mean kurtosis among attributes of the numeric type.

2 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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