DEVELOPMENT... OpenML
Data
dionis_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

dionis_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Subsampling of the dataset dionis (41167) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10, stratified: bool = True, ) -> Dataset: rng = np.random.default_rng(seed) x = self.x y = self.y # Uniformly sample classes = y.unique() if len(classes) > nclasses_max: vcs = y.value_counts() selected_classes = rng.choice( classes, size=nclasses_max, replace=False, p=vcs / sum(vcs), ) # Select the indices where one of these classes is present idxs = y.index[y.isin(classes)] x = x.iloc[idxs] y = y.iloc[idxs] # Uniformly sample columns if required if len(x.columns) > ncols_max: columns_idxs = rng.choice( list(range(len(x.columns))), size=ncols_max, replace=False ) sorted_column_idxs = sorted(columns_idxs) selected_columns = list(x.columns[sorted_column_idxs]) x = x[selected_columns] else: sorted_column_idxs = list(range(len(x.columns))) if len(x) > nrows_max: # Stratify accordingly target_name = y.name data = pd.concat((x, y), axis="columns") _, subset = train_test_split( data, test_size=nrows_max, stratify=data[target_name], shuffle=True, random_state=seed, ) x = subset.drop(target_name, axis="columns") y = subset[target_name] # We need to convert categorical columns to string for openml categorical_mask = [self.categorical_mask[i] for i in sorted_column_idxs] columns = list(x.columns) return Dataset( # Technically this is not the same but it's where it was derived from dataset=self.dataset, x=x, y=y, categorical_mask=categorical_mask, columns=columns, ) ```

61 features

class (target)nominal355 unique values
0 missing
V32numeric1260 unique values
0 missing
V31numeric1461 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric1610 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric1522 unique values
0 missing
V37numeric1 unique values
0 missing
V38numeric1757 unique values
0 missing
V39numeric1485 unique values
0 missing
V40numeric1506 unique values
0 missing
V41numeric1813 unique values
0 missing
V42numeric1812 unique values
0 missing
V43numeric1626 unique values
0 missing
V44numeric1262 unique values
0 missing
V45numeric816 unique values
0 missing
V46numeric1736 unique values
0 missing
V47numeric770 unique values
0 missing
V48numeric1802 unique values
0 missing
V49numeric712 unique values
0 missing
V50numeric1855 unique values
0 missing
V51numeric1488 unique values
0 missing
V52numeric1276 unique values
0 missing
V53numeric13 unique values
0 missing
V54numeric1 unique values
0 missing
V55numeric1526 unique values
0 missing
V56numeric1754 unique values
0 missing
V57numeric1699 unique values
0 missing
V58numeric1308 unique values
0 missing
V59numeric1363 unique values
0 missing
V60numeric668 unique values
0 missing
V16numeric1459 unique values
0 missing
V2numeric1626 unique values
0 missing
V3numeric27 unique values
0 missing
V4numeric1811 unique values
0 missing
V5numeric1161 unique values
0 missing
V6numeric26 unique values
0 missing
V7numeric1319 unique values
0 missing
V8numeric1846 unique values
0 missing
V9numeric1231 unique values
0 missing
V10numeric745 unique values
0 missing
V11numeric1686 unique values
0 missing
V12numeric1536 unique values
0 missing
V13numeric1804 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric1830 unique values
0 missing
V1numeric1522 unique values
0 missing
V17numeric1598 unique values
0 missing
V18numeric1506 unique values
0 missing
V19numeric1624 unique values
0 missing
V20numeric1073 unique values
0 missing
V21numeric962 unique values
0 missing
V22numeric979 unique values
0 missing
V23numeric1816 unique values
0 missing
V24numeric1473 unique values
0 missing
V25numeric620 unique values
0 missing
V26numeric879 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric1744 unique values
0 missing
V29numeric1619 unique values
0 missing
V30numeric1643 unique values
0 missing
Show all 61 features

19 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
61
Number of attributes (columns) of the dataset.
NumberOfClasses
355
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
60
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfSymbolicFeatures
1.64
Percentage of nominal attributes.
AutoCorrelation
0
Average class difference between consecutive instances.
PercentageOfNumericFeatures
98.36
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
NumberOfBinaryFeatures
0
Number of binary attributes.
MinorityClassSize
4
Number of instances belonging to the least frequent class.
MinorityClassPercentage
0.2
Percentage of instances belonging to the least frequent class.
MajorityClassSize
12
Number of instances belonging to the most frequent class.
MajorityClassPercentage
0.6
Percentage of instances belonging to the most frequent class.
Dimensionality
0.03
Number of attributes divided by the number of instances.
Show all 19 properties

0 tasks

Define a new task