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adult_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

adult_seed_4_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 adult (1590) with seed=4 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, ) ```

15 features

class (target)nominal2 unique values
0 missing
agenumeric65 unique values
0 missing
workclassnominal6 unique values
112 missing
fnlwgtnumeric1937 unique values
0 missing
educationnominal16 unique values
0 missing
education-numnumeric16 unique values
0 missing
marital-statusnominal7 unique values
0 missing
occupationnominal13 unique values
112 missing
relationshipnominal6 unique values
0 missing
racenominal5 unique values
0 missing
sexnominal2 unique values
0 missing
capital-gainnumeric56 unique values
0 missing
capital-lossnumeric36 unique values
0 missing
hours-per-weeknumeric66 unique values
0 missing
native-countrynominal33 unique values
29 missing

19 properties

2000
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
253
Number of missing values in the dataset.
140
Number of instances with at least one value missing.
6
Number of numeric attributes.
9
Number of nominal attributes.
13.33
Percentage of binary attributes.
7
Percentage of instances having missing values.
0.84
Percentage of missing values.
0.62
Average class difference between consecutive instances.
40
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
60
Percentage of nominal attributes.
76.05
Percentage of instances belonging to the most frequent class.
1521
Number of instances belonging to the most frequent class.
23.95
Percentage of instances belonging to the least frequent class.
479
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

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