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shuttle_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

shuttle_seed_4_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=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, ) ```

10 features

class (target)nominal5 unique values
0 missing
A1numeric55 unique values
0 missing
A2numeric37 unique values
0 missing
A3numeric42 unique values
0 missing
A4numeric28 unique values
0 missing
A5numeric47 unique values
0 missing
A6numeric74 unique values
0 missing
A7numeric64 unique values
0 missing
A8numeric98 unique values
0 missing
A9numeric60 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 binary attributes.
0
Percentage of instances having missing values.
0.64
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
90
Percentage of numeric attributes.
78.6
Percentage of instances belonging to the most frequent class.
10
Percentage of nominal attributes.
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.

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