Data
jannis

jannis

active ARFF Publicly available Visibility: public Uploaded 03-01-2023 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original link: https://openml.org/d/41168 Original description: 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.

55 features

class (target)numeric2 unique values
0 missing
V1numeric38700 unique values
0 missing
V2numeric841 unique values
0 missing
V3numeric864 unique values
0 missing
V4numeric54991 unique values
0 missing
V5numeric55857 unique values
0 missing
V6numeric53873 unique values
0 missing
V7numeric55093 unique values
0 missing
V8numeric54814 unique values
0 missing
V9numeric54199 unique values
0 missing
V10numeric52203 unique values
0 missing
V11numeric52571 unique values
0 missing
V12numeric54008 unique values
0 missing
V13numeric55057 unique values
0 missing
V14numeric55103 unique values
0 missing
V15numeric55330 unique values
0 missing
V16numeric56741 unique values
0 missing
V17numeric56757 unique values
0 missing
V18numeric56630 unique values
0 missing
V19numeric54597 unique values
0 missing
V20numeric56857 unique values
0 missing
V21numeric56520 unique values
0 missing
V22numeric54210 unique values
0 missing
V23numeric55698 unique values
0 missing
V24numeric55839 unique values
0 missing
V25numeric56704 unique values
0 missing
V26numeric56742 unique values
0 missing
V27numeric56528 unique values
0 missing
V28numeric54608 unique values
0 missing
V29numeric845 unique values
0 missing
V30numeric56873 unique values
0 missing
V31numeric55325 unique values
0 missing
V32numeric55301 unique values
0 missing
V33numeric53763 unique values
0 missing
V34numeric55308 unique values
0 missing
V35numeric56898 unique values
0 missing
V36numeric56481 unique values
0 missing
V37numeric51987 unique values
0 missing
V38numeric866 unique values
0 missing
V39numeric52030 unique values
0 missing
V40numeric55377 unique values
0 missing
V41numeric56654 unique values
0 missing
V42numeric55255 unique values
0 missing
V43numeric53813 unique values
0 missing
V44numeric52647 unique values
0 missing
V45numeric55276 unique values
0 missing
V46numeric55043 unique values
0 missing
V47numeric54146 unique values
0 missing
V48numeric54894 unique values
0 missing
V49numeric52028 unique values
0 missing
V50numeric56512 unique values
0 missing
V51numeric55504 unique values
0 missing
V52numeric53777 unique values
0 missing
V53numeric38799 unique values
0 missing
V54numeric54290 unique values
0 missing

19 properties

57580
Number of instances (rows) of the dataset.
55
Number of attributes (columns) of the dataset.
0
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.
55
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.

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