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sylvine

sylvine

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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.

21 features

class (target)nominal2 unique values
0 missing
V1numeric157 unique values
0 missing
V2numeric364 unique values
0 missing
V3numeric118 unique values
0 missing
V4numeric2549 unique values
0 missing
V5numeric360 unique values
0 missing
V6numeric2574 unique values
0 missing
V7numeric1183 unique values
0 missing
V8numeric48 unique values
0 missing
V9numeric2620 unique values
0 missing
V10numeric389 unique values
0 missing
V11numeric120 unique values
0 missing
V12numeric151 unique values
0 missing
V13numeric146 unique values
0 missing
V14numeric2556 unique values
0 missing
V15numeric2218 unique values
0 missing
V16numeric393 unique values
0 missing
V17numeric122 unique values
0 missing
V18numeric360 unique values
0 missing
V19numeric361 unique values
0 missing
V20numeric226 unique values
0 missing

62 properties

5124
Number of instances (rows) of the dataset.
21
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.
20
Number of numeric attributes.
1
Number of nominal attributes.
-1.17
Minimum skewness among attributes of the numeric type.
4.76
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
7.29
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.89
Third quartile of skewness among attributes of the numeric type.
1.76
Maximum skewness among attributes of the numeric type.
50
Percentage of instances belonging to the least frequent class.
-0.43
First quartile of kurtosis among attributes of the numeric type.
980.45
Third quartile of standard deviation of attributes of the numeric type.
1563.37
Maximum standard deviation of attributes of the numeric type.
2562
Number of instances belonging to the least frequent class.
153.86
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.93
Mean kurtosis among attributes of the numeric type.
-1.05
First quartile of skewness among attributes of the numeric type.
857.3
Mean of means among attributes of the numeric type.
25.37
First quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
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.
Second quartile (Median) of entropy among attributes.
1
Entropy of the target attribute values.
2
Average number of distinct values among the attributes of the nominal type.
1.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
0.11
Mean skewness among attributes of the numeric type.
218.47
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.
422.44
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
50
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.44
Second quartile (Median) of skewness among attributes of the numeric type.
2562
Number of instances belonging to the most frequent class.
-1.22
Minimum kurtosis among attributes of the numeric type.
4.76
Percentage of binary attributes.
90.67
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
14.06
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
4.52
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
1.83
Third quartile of kurtosis among attributes of the numeric type.
3150.84
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
95.24
Percentage of numeric attributes.
2267.27
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.

20 tasks

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