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shuttle_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

shuttle_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF public Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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Subsampling of the dataset shuttle (40685) 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, ) ```

10 features

class (target)nominal5 unique values
0 missing
A1numeric55 unique values
0 missing
A2numeric28 unique values
0 missing
A3numeric43 unique values
0 missing
A4numeric27 unique values
0 missing
A5numeric47 unique values
0 missing
A6numeric77 unique values
0 missing
A7numeric63 unique values
0 missing
A8numeric105 unique values
0 missing
A9numeric65 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
5
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.
9
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of missing values.
0.65
Average class difference between consecutive instances.
90
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
10
Percentage of nominal attributes.
78.6
Percentage of instances belonging to the most frequent class.
1572
Number of instances belonging to the most frequent class.
0.1
Percentage of instances belonging to the least frequent class.
2
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.

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