Data
ada_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

ada_seed_1_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 ada (41156) with seed=1 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, ) ```

49 features

class (target)nominal2 unique values
0 missing
V1numeric2 unique values
0 missing
V2numeric2 unique values
0 missing
V3numeric2 unique values
0 missing
V4numeric300 unique values
0 missing
V5numeric2 unique values
0 missing
V6numeric2 unique values
0 missing
V7numeric2 unique values
0 missing
V8numeric2 unique values
0 missing
V9numeric2 unique values
0 missing
V10numeric34 unique values
0 missing
V11numeric2 unique values
0 missing
V12numeric2 unique values
0 missing
V13numeric2 unique values
0 missing
V14numeric1 unique values
0 missing
V15numeric16 unique values
0 missing
V16numeric2 unique values
0 missing
V17numeric2 unique values
0 missing
V18numeric2 unique values
0 missing
V19numeric2 unique values
0 missing
V20numeric2 unique values
0 missing
V21numeric1 unique values
0 missing
V22numeric2 unique values
0 missing
V23numeric2 unique values
0 missing
V24numeric2 unique values
0 missing
V25numeric66 unique values
0 missing
V26numeric2 unique values
0 missing
V27numeric2 unique values
0 missing
V28numeric2 unique values
0 missing
V29numeric2 unique values
0 missing
V30numeric2 unique values
0 missing
V31numeric2 unique values
0 missing
V32numeric41 unique values
0 missing
V33numeric2 unique values
0 missing
V34numeric2 unique values
0 missing
V35numeric2 unique values
0 missing
V36numeric2 unique values
0 missing
V37numeric2 unique values
0 missing
V38numeric2 unique values
0 missing
V39numeric2 unique values
0 missing
V40numeric65 unique values
0 missing
V41numeric2 unique values
0 missing
V42numeric2 unique values
0 missing
V43numeric2 unique values
0 missing
V44numeric2 unique values
0 missing
V45numeric2 unique values
0 missing
V46numeric2 unique values
0 missing
V47numeric1 unique values
0 missing
V48numeric2 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
49
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.
48
Number of numeric attributes.
1
Number of nominal attributes.
2.04
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.62
Average class difference between consecutive instances.
0
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
97.96
Percentage of numeric attributes.
75.2
Percentage of instances belonging to the most frequent class.
2.04
Percentage of nominal attributes.
1504
Number of instances belonging to the most frequent class.
24.8
Percentage of instances belonging to the least frequent class.
496
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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