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dionis_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

dionis_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
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Subsampling of the dataset dionis (41167) with seed=0 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
V32numeric1280 unique values
0 missing
V31numeric1477 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric1625 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric1505 unique values
0 missing
V37numeric1 unique values
0 missing
V38numeric1744 unique values
0 missing
V39numeric1461 unique values
0 missing
V40numeric1493 unique values
0 missing
V41numeric1783 unique values
0 missing
V42numeric1816 unique values
0 missing
V43numeric1620 unique values
0 missing
V44numeric1250 unique values
0 missing
V45numeric815 unique values
0 missing
V46numeric1741 unique values
0 missing
V47numeric785 unique values
0 missing
V48numeric1801 unique values
0 missing
V49numeric739 unique values
0 missing
V50numeric1859 unique values
0 missing
V51numeric1503 unique values
0 missing
V52numeric1304 unique values
0 missing
V53numeric14 unique values
0 missing
V54numeric1 unique values
0 missing
V55numeric1544 unique values
0 missing
V56numeric1766 unique values
0 missing
V57numeric1717 unique values
0 missing
V58numeric1310 unique values
0 missing
V59numeric1353 unique values
0 missing
V60numeric634 unique values
0 missing
V16numeric1443 unique values
0 missing
V2numeric1649 unique values
0 missing
V3numeric25 unique values
0 missing
V4numeric1836 unique values
0 missing
V5numeric1154 unique values
0 missing
V6numeric24 unique values
0 missing
V7numeric1340 unique values
0 missing
V8numeric1837 unique values
0 missing
V9numeric1287 unique values
0 missing
V10numeric742 unique values
0 missing
V11numeric1664 unique values
0 missing
V12numeric1536 unique values
0 missing
V13numeric1786 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric1853 unique values
0 missing
V1numeric1505 unique values
0 missing
V17numeric1610 unique values
0 missing
V18numeric1550 unique values
0 missing
V19numeric1641 unique values
0 missing
V20numeric1056 unique values
0 missing
V21numeric996 unique values
0 missing
V22numeric983 unique values
0 missing
V23numeric1836 unique values
0 missing
V24numeric1514 unique values
0 missing
V25numeric627 unique values
0 missing
V26numeric894 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric1728 unique values
0 missing
V29numeric1633 unique values
0 missing
V30numeric1671 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

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