Data
dionis_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

dionis_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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Subsampling of the dataset dionis (41167) with seed=2 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
V1numeric1514 unique values
0 missing
V2numeric1640 unique values
0 missing
V3numeric26 unique values
0 missing
V4numeric1837 unique values
0 missing
V5numeric1180 unique values
0 missing
V6numeric26 unique values
0 missing
V7numeric1352 unique values
0 missing
V8numeric1876 unique values
0 missing
V9numeric1275 unique values
0 missing
V10numeric751 unique values
0 missing
V11numeric1666 unique values
0 missing
V12numeric1576 unique values
0 missing
V13numeric1777 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric1850 unique values
0 missing
V16numeric1437 unique values
0 missing
V17numeric1588 unique values
0 missing
V18numeric1529 unique values
0 missing
V19numeric1651 unique values
0 missing
V20numeric1086 unique values
0 missing
V21numeric970 unique values
0 missing
V22numeric1000 unique values
0 missing
V23numeric1851 unique values
0 missing
V24numeric1528 unique values
0 missing
V25numeric627 unique values
0 missing
V26numeric910 unique values
0 missing
V27numeric1 unique values
0 missing
V28numeric1730 unique values
0 missing
V29numeric1647 unique values
0 missing
V30numeric1633 unique values
0 missing
V31numeric1465 unique values
0 missing
V32numeric1287 unique values
0 missing
V33numeric1 unique values
0 missing
V34numeric1631 unique values
0 missing
V35numeric1 unique values
0 missing
V36numeric1514 unique values
0 missing
V37numeric1 unique values
0 missing
V38numeric1719 unique values
0 missing
V39numeric1513 unique values
0 missing
V40numeric1511 unique values
0 missing
V41numeric1803 unique values
0 missing
V42numeric1803 unique values
0 missing
V43numeric1640 unique values
0 missing
V44numeric1258 unique values
0 missing
V45numeric825 unique values
0 missing
V46numeric1743 unique values
0 missing
V47numeric790 unique values
0 missing
V48numeric1801 unique values
0 missing
V49numeric729 unique values
0 missing
V50numeric1850 unique values
0 missing
V51numeric1472 unique values
0 missing
V52numeric1306 unique values
0 missing
V53numeric15 unique values
0 missing
V54numeric1 unique values
0 missing
V55numeric1551 unique values
0 missing
V56numeric1778 unique values
0 missing
V57numeric1713 unique values
0 missing
V58numeric1282 unique values
0 missing
V59numeric1390 unique values
0 missing
V60numeric663 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
355
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.
60
Number of numeric attributes.
1
Number of nominal attributes.
0.03
Number of attributes divided by the number of instances.
98.36
Percentage of numeric attributes.
0.6
Percentage of instances belonging to the most frequent class.
1.64
Percentage of nominal attributes.
12
Number of instances belonging to the most frequent class.
0.2
Percentage of instances belonging to the least frequent class.
4
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Average class difference between consecutive instances.
0
Percentage of missing values.

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