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fabert

active ARFF Publicly available Visibility: public Uploaded 16-08-2018 by Heidi Smith
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SOURCE: [ChaLearn Automatic Machine Learning Challenge (AutoML)](https://competitions.codalab.org/competitions/2321), [ChaLearn](https://automl.chalearn.org/data) This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed number of numerical coefficients). The challenge is to solve classification and regression problems, without any further human intervention. The difficulty is that there is a broad diversity of data types and distributions (including balanced or unbalanced classes, sparse or dense feature representations, with or without missing values or categorical variables, various metrics of evaluation, various proportions of number of features and number of examples). The problems are drawn from a wide variety of domains and include medical diagnosis from laboratory analyses, speech recognition, credit rating, prediction or drug toxicity or efficacy, classification of text, prediction of customer satisfaction, object recognition, protein structure prediction, action recognition in video data, etc. While there exist machine learning toolkits including methods that can solve all these problems, it is still considerable human effort to find, for a given combination of dataset, task, metric of evaluation, and available computational time, the combination of methods and hyper-parameter setting that is best suited. Your challenge is to create the "perfect black box" eliminating the human in the loop. This is a challenge with code submission: your code will be executed automatically on our servers to train and test your learning machines with unknown datasets. However, there is NO OBLIGATION TO SUBMIT CODE. Half of the prizes can be won by just submitting prediction results. There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is NO PREREQUISITE TO PARTICIPATE IN PREVIOUS ROUNDS to enter a new round. The rounds alternate AutoML phases in which submitted code is "blind tested" in limited time on our platform, using datasets you have never seen before, and Tweakathon phases giving you time to improve your methods by tweaking them on those datasets and running them on your own systems (without computational resource limitation). NOTE: This dataset corresponds to one of the datasets of the challenge.

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0 missing
V632numeric103 unique values
0 missing
V633numeric52 unique values
0 missing
V634numeric47 unique values
0 missing
V635numeric55 unique values
0 missing
V636numeric28 unique values
0 missing
V637numeric26 unique values
0 missing
V638numeric13 unique values
0 missing
V639numeric114 unique values
0 missing
V640numeric74 unique values
0 missing
V641numeric81 unique values
0 missing
V642numeric32 unique values
0 missing
V643numeric58 unique values
0 missing
V644numeric55 unique values
0 missing
V645numeric20 unique values
0 missing
V646numeric30 unique values
0 missing
V647numeric44 unique values
0 missing
V648numeric24 unique values
0 missing
V649numeric74 unique values
0 missing
V650numeric10 unique values
0 missing
V651numeric32 unique values
0 missing
V652numeric54 unique values
0 missing
V653numeric21 unique values
0 missing
V654numeric123 unique values
0 missing
V655numeric7 unique values
0 missing
V656numeric13 unique values
0 missing
V657numeric33 unique values
0 missing
V658numeric62 unique values
0 missing
V659numeric8 unique values
0 missing
V660numeric75 unique values
0 missing
V661numeric29 unique values
0 missing
V662numeric24 unique values
0 missing
V663numeric43 unique values
0 missing
V664numeric107 unique values
0 missing
V665numeric18 unique values
0 missing
V666numeric39 unique values
0 missing
V667numeric55 unique values
0 missing
V668numeric3 unique values
0 missing
V669numeric84 unique values
0 missing
V670numeric32 unique values
0 missing
V671numeric54 unique values
0 missing
V672numeric63 unique values
0 missing
V673numeric5 unique values
0 missing
V674numeric17 unique values
0 missing
V675numeric34 unique values
0 missing
V676numeric56 unique values
0 missing
V677numeric68 unique values
0 missing
V678numeric50 unique values
0 missing
V679numeric57 unique values
0 missing
V680numeric49 unique values
0 missing
V681numeric125 unique values
0 missing
V682numeric43 unique values
0 missing
V683numeric26 unique values
0 missing
V684numeric35 unique values
0 missing
V685numeric120 unique values
0 missing
V686numeric10 unique values
0 missing
V687numeric52 unique values
0 missing
V688numeric24 unique values
0 missing
V689numeric67 unique values
0 missing
V690numeric18 unique values
0 missing
V691numeric7 unique values
0 missing
V692numeric23 unique values
0 missing
V693numeric58 unique values
0 missing
V694numeric81 unique values
0 missing
V695numeric103 unique values
0 missing
V696numeric20 unique values
0 missing
V697numeric83 unique values
0 missing
V698numeric59 unique values
0 missing
V699numeric63 unique values
0 missing
V700numeric52 unique values
0 missing
V701numeric40 unique values
0 missing
V702numeric29 unique values
0 missing
V703numeric29 unique values
0 missing
V704numeric26 unique values
0 missing
V705numeric30 unique values
0 missing
V706numeric22 unique values
0 missing
V707numeric18 unique values
0 missing
V708numeric17 unique values
0 missing
V709numeric101 unique values
0 missing
V710numeric33 unique values
0 missing
V711numeric39 unique values
0 missing
V712numeric42 unique values
0 missing
V713numeric2 unique values
0 missing
V714numeric25 unique values
0 missing
V715numeric31 unique values
0 missing
V716numeric28 unique values
0 missing
V717numeric69 unique values
0 missing
V718numeric20 unique values
0 missing
V719numeric11 unique values
0 missing
V720numeric14 unique values
0 missing
V721numeric47 unique values
0 missing
V722numeric13 unique values
0 missing
V723numeric61 unique values
0 missing
V724numeric28 unique values
0 missing
V725numeric63 unique values
0 missing
V726numeric137 unique values
0 missing
V727numeric15 unique values
0 missing
V728numeric10 unique values
0 missing
V729numeric61 unique values
0 missing
V730numeric91 unique values
0 missing
V731numeric17 unique values
0 missing
V732numeric18 unique values
0 missing
V733numeric59 unique values
0 missing
V734numeric103 unique values
0 missing
V735numeric18 unique values
0 missing
V736numeric41 unique values
0 missing
V737numeric4 unique values
0 missing
V738numeric41 unique values
0 missing
V739numeric7 unique values
0 missing
V740numeric7 unique values
0 missing
V741numeric44 unique values
0 missing
V742numeric72 unique values
0 missing
V743numeric107 unique values
0 missing
V744numeric64 unique values
0 missing
V745numeric73 unique values
0 missing
V746numeric36 unique values
0 missing
V747numeric62 unique values
0 missing
V748numeric125 unique values
0 missing
V749numeric57 unique values
0 missing
V750numeric78 unique values
0 missing
V751numeric2 unique values
0 missing
V752numeric109 unique values
0 missing
V753numeric49 unique values
0 missing
V754numeric13 unique values
0 missing
V755numeric2 unique values
0 missing
V756numeric120 unique values
0 missing
V757numeric40 unique values
0 missing
V758numeric35 unique values
0 missing
V759numeric5 unique values
0 missing
V760numeric26 unique values
0 missing
V761numeric59 unique values
0 missing
V762numeric20 unique values
0 missing
V763numeric83 unique values
0 missing
V764numeric56 unique values
0 missing
V765numeric27 unique values
0 missing
V766numeric15 unique values
0 missing
V767numeric66 unique values
0 missing
V768numeric16 unique values
0 missing
V769numeric114 unique values
0 missing
V770numeric105 unique values
0 missing
V771numeric76 unique values
0 missing
V772numeric2 unique values
0 missing
V773numeric33 unique values
0 missing
V774numeric19 unique values
0 missing
V775numeric56 unique values
0 missing
V776numeric34 unique values
0 missing
V777numeric97 unique values
0 missing
V778numeric96 unique values
0 missing
V779numeric129 unique values
0 missing
V780numeric86 unique values
0 missing
V781numeric80 unique values
0 missing
V782numeric32 unique values
0 missing
V783numeric52 unique values
0 missing
V784numeric14 unique values
0 missing
V785numeric2 unique values
0 missing
V786numeric25 unique values
0 missing
V787numeric55 unique values
0 missing
V788numeric45 unique values
0 missing
V789numeric8 unique values
0 missing
V790numeric52 unique values
0 missing
V791numeric92 unique values
0 missing
V792numeric54 unique values
0 missing
V793numeric138 unique values
0 missing
V794numeric19 unique values
0 missing
V795numeric83 unique values
0 missing
V796numeric32 unique values
0 missing
V797numeric33 unique values
0 missing
V798numeric3 unique values
0 missing
V799numeric76 unique values
0 missing
V800numeric31 unique values
0 missing

62 properties

8237
Number of instances (rows) of the dataset.
801
Number of attributes (columns) of the dataset.
7
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.
800
Number of numeric attributes.
1
Number of nominal attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
99.88
Percentage of numeric attributes.
0.12
Percentage of nominal attributes.
First quartile of entropy among attributes.
154.78
First quartile of kurtosis among attributes of the numeric type.
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.
12
First quartile of skewness among attributes of the numeric type.
0.04
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
224.4
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
14.37
Second quartile (Median) of skewness among attributes of the numeric type.
0.06
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
360.4
Third quartile of kurtosis among attributes of the numeric type.
0.01
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
18.29
Third quartile of skewness among attributes of the numeric type.
0.07
Third quartile of standard deviation of attributes of the numeric type.
0.17
Average class difference between consecutive instances.
0.01
Mean of means among attributes of the numeric type.
2.7
Entropy of the target attribute values.
0.1
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
23.39
Percentage of instances belonging to the most frequent class.
1927
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
8237
Maximum kurtosis among attributes of the numeric type.
0.02
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
90.76
Maximum skewness among attributes of the numeric type.
0.11
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
415.56
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
7
Average number of distinct values among the attributes of the nominal type.
16.98
Mean skewness among attributes of the numeric type.
0.06
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
45.63
Minimum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
7
The minimal number of distinct values among attributes of the nominal type.
6.22
Minimum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
6.09
Percentage of instances belonging to the least frequent class.
502
Number of instances belonging to the least frequent class.

19 tasks

4 runs - estimation_procedure: 33% Holdout set - target_feature: class
3 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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
Define a new task

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