Data
youtube

youtube

active ARFF Publicly available Visibility: public Uploaded 14-03-2019 by Quay Au
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  • 2019_multioutput_paper Education Health
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The YouTube personality dataset consists of a collection of behavorial features, speech transcriptions, and personality impression scores for a set of 404 YouTube vloggers that explicitly show themselves in front of the a webcam talking about a variety of topics including personal issues, politics, movies, books, etc. There is no content-related restriction and the language used in the videos is natural and diverse.

31 features

gender (target)nominal2 unique values
0 missing
Extr (target)numeric60 unique values
0 missing
Agr (target)numeric59 unique values
0 missing
Cons (target)numeric55 unique values
0 missing
Emot (target)numeric57 unique values
0 missing
Open (target)numeric51 unique values
0 missing
mean.pitchnumeric395 unique values
0 missing
sd.pitchnumeric398 unique values
0 missing
mean.conf.pitchnumeric399 unique values
0 missing
sd.conf.pitchnumeric400 unique values
0 missing
mean.spec.entropynumeric393 unique values
0 missing
sd.spec.entropynumeric401 unique values
0 missing
mean.val.apeaknumeric400 unique values
0 missing
sd.val.apeaknumeric395 unique values
0 missing
mean.loc.apeaknumeric400 unique values
0 missing
sd.loc.apeaknumeric402 unique values
0 missing
mean.num.apeaknumeric399 unique values
0 missing
sd.num.apeaknumeric399 unique values
0 missing
mean.energynumeric401 unique values
0 missing
sd.energynumeric397 unique values
0 missing
mean.d.energynumeric402 unique values
0 missing
sd.d.energynumeric403 unique values
0 missing
avg.voiced.segnumeric401 unique values
0 missing
avg.len.segnumeric396 unique values
0 missing
time.speakingnumeric376 unique values
0 missing
voice.ratenumeric402 unique values
0 missing
num.turnsnumeric123 unique values
0 missing
hogv.entropynumeric404 unique values
0 missing
hogv.mediannumeric370 unique values
0 missing
hogv.cogRnumeric81 unique values
0 missing
hogv.cogCnumeric88 unique values
0 missing

62 properties

404
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
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.
30
Number of numeric attributes.
1
Number of nominal attributes.
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.
Second quartile (Median) of entropy among attributes.
0.08
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
1.8
Second quartile (Median) of kurtosis 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.
0.79
Mean skewness among attributes of the numeric type.
0.96
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
3.63
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.27
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.58
Minimum kurtosis among attributes of the numeric type.
3.23
Percentage of binary attributes.
0.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
192.26
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
215.9
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
8.53
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
96.77
Percentage of numeric attributes.
4.67
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-7.93
Minimum skewness among attributes of the numeric type.
3.23
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
13.57
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.26
Third quartile of skewness among attributes of the numeric type.
54.96
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.56
First quartile of kurtosis among attributes of the numeric type.
1.08
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.28
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.
20.37
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
18.68
Mean of means among attributes of the numeric type.
-0.5
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.09
First quartile of standard deviation of attributes of the numeric type.

9 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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