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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
V851numeric441 unique values
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0 missing
V881numeric442 unique values
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0 missing
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0 missing
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V890numeric489 unique values
0 missing
V891numeric179 unique values
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0 missing
V893numeric357 unique values
0 missing
V894numeric474 unique values
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V895numeric637 unique values
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0 missing
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0 missing
V940numeric391 unique values
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V941numeric82 unique values
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V942numeric332 unique values
0 missing
V943numeric356 unique values
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V944numeric243 unique values
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V946numeric259 unique values
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V947numeric465 unique values
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V948numeric410 unique values
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V949numeric524 unique values
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V951nominal2 unique values
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V978numeric581 unique values
0 missing
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0 missing
V980numeric440 unique values
0 missing
V981numeric390 unique values
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V982numeric678 unique values
0 missing
V983numeric384 unique values
0 missing
V984numeric518 unique values
0 missing
V985numeric198 unique values
0 missing
V986numeric386 unique values
0 missing
V987numeric13 unique values
0 missing
V988numeric45 unique values
0 missing
V989numeric415 unique values
0 missing
V990numeric440 unique values
0 missing
V991numeric450 unique values
0 missing
V992numeric494 unique values
0 missing
V993numeric538 unique values
0 missing
V994numeric21 unique values
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0 missing
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0 missing
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0 missing

62 properties

5418
Number of instances (rows) of the dataset.
1637
Number of attributes (columns) of the dataset.
2
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.
1599
Number of numeric attributes.
38
Number of nominal attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.79
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
97.68
Percentage of numeric attributes.
2.32
Percentage of nominal attributes.
0
First quartile of entropy among attributes.
0.26
First quartile of kurtosis among attributes of the numeric type.
144.4
First quartile of means among attributes of the numeric type.
0.48
Standard deviation of the number of distinct values among attributes of the nominal type.
0.1
First quartile of skewness among attributes of the numeric type.
63.62
First quartile of standard deviation of attributes of the numeric type.
0
Second quartile (Median) of entropy among attributes.
1.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
254.89
Second quartile (Median) of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.6
Second quartile (Median) of skewness among attributes of the numeric type.
85
Second quartile (Median) of standard deviation of attributes of the numeric type.
0
Third quartile of entropy among attributes.
5.13
Third quartile of kurtosis among attributes of the numeric type.
367.41
Third quartile of means among attributes of the numeric type.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
1.4
Third quartile of skewness among attributes of the numeric type.
109.36
Third quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
283.63
Mean of means among attributes of the numeric type.
1
Entropy of the target attribute values.
0.3
Number of attributes divided by the number of instances.
3250.7
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
50
Percentage of instances belonging to the most frequent class.
2709
Number of instances belonging to the most frequent class.
0.45
Maximum entropy among attributes.
3692.49
Maximum kurtosis among attributes of the numeric type.
984.17
Maximum of means among attributes of the numeric type.
0.01
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
58.89
Maximum skewness among attributes of the numeric type.
297.63
Maximum standard deviation of attributes of the numeric type.
0.08
Average entropy of the attributes.
22.49
Mean kurtosis among attributes of the numeric type.
13
Number of binary attributes.
0
Average mutual information between the nominal attributes and the target attribute.
251.85
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.34
Average number of distinct values among the attributes of the nominal type.
1.19
Mean skewness among attributes of the numeric type.
87.32
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
-1.71
Minimum kurtosis among attributes of the numeric type.
0.21
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
-11.99
Minimum skewness among attributes of the numeric type.
0.47
Minimum standard deviation of attributes of the numeric type.
50
Percentage of instances belonging to the least frequent class.
2709
Number of instances belonging to the least frequent class.

20 tasks

4 runs - estimation_procedure: 33% Holdout set - target_feature: class
4 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - 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: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - 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
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