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helena_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

helena_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 helena (41169) 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, ) ```

28 features

class (target)nominal100 unique values
0 missing
V1numeric1964 unique values
0 missing
V2numeric542 unique values
0 missing
V3numeric606 unique values
0 missing
V4numeric1998 unique values
0 missing
V5numeric1999 unique values
0 missing
V6numeric1996 unique values
0 missing
V7numeric1997 unique values
0 missing
V8numeric1997 unique values
0 missing
V9numeric1992 unique values
0 missing
V10numeric1988 unique values
0 missing
V11numeric1994 unique values
0 missing
V12numeric1994 unique values
0 missing
V13numeric1998 unique values
0 missing
V14numeric1997 unique values
0 missing
V15numeric1999 unique values
0 missing
V16numeric1998 unique values
0 missing
V17numeric1999 unique values
0 missing
V18numeric1998 unique values
0 missing
V19numeric1999 unique values
0 missing
V20numeric1999 unique values
0 missing
V21numeric2000 unique values
0 missing
V22numeric1996 unique values
0 missing
V23numeric1998 unique values
0 missing
V24numeric1998 unique values
0 missing
V25numeric2000 unique values
0 missing
V26numeric2000 unique values
0 missing
V27numeric1997 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
100
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.
27
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.02
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
96.43
Percentage of numeric attributes.
6.15
Percentage of instances belonging to the most frequent class.
3.57
Percentage of nominal attributes.
123
Number of instances belonging to the most frequent class.
0.15
Percentage of instances belonging to the least frequent class.
3
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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