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qsar-biodeg_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

qsar-biodeg_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
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Subsampling of the dataset qsar-biodeg (1494) 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, ) ```

42 features

Class (target)nominal2 unique values
0 missing
V23numeric13 unique values
0 missing
V22numeric352 unique values
0 missing
V24numeric2 unique values
0 missing
V25numeric2 unique values
0 missing
V26numeric4 unique values
0 missing
V27numeric329 unique values
0 missing
V28numeric205 unique values
0 missing
V29numeric2 unique values
0 missing
V30numeric470 unique values
0 missing
V31numeric553 unique values
0 missing
V32numeric8 unique values
0 missing
V33numeric11 unique values
0 missing
V34numeric16 unique values
0 missing
V35numeric8 unique values
0 missing
V36numeric705 unique values
0 missing
V37numeric624 unique values
0 missing
V38numeric8 unique values
0 missing
V39numeric862 unique values
0 missing
V40numeric5 unique values
0 missing
V41numeric17 unique values
0 missing
V12numeric384 unique values
0 missing
V2numeric1022 unique values
0 missing
V3numeric11 unique values
0 missing
V4numeric4 unique values
0 missing
V5numeric16 unique values
0 missing
V6numeric13 unique values
0 missing
V7numeric15 unique values
0 missing
V8numeric188 unique values
0 missing
V9numeric15 unique values
0 missing
V10numeric12 unique values
0 missing
V11numeric21 unique values
0 missing
V1numeric440 unique values
0 missing
V13numeric756 unique values
0 missing
V14numeric373 unique values
0 missing
V15numeric510 unique values
0 missing
V16numeric24 unique values
0 missing
V17numeric167 unique values
0 missing
V18numeric125 unique values
0 missing
V19numeric3 unique values
0 missing
V20numeric4 unique values
0 missing
V21numeric4 unique values
0 missing

19 properties

1055
Number of instances (rows) of the dataset.
42
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.
41
Number of numeric attributes.
1
Number of nominal attributes.
2.38
Percentage of nominal attributes.
1
Average class difference between consecutive instances.
97.62
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
2.38
Percentage of binary attributes.
1
Number of binary attributes.
356
Number of instances belonging to the least frequent class.
33.74
Percentage of instances belonging to the least frequent class.
699
Number of instances belonging to the most frequent class.
66.26
Percentage of instances belonging to the most frequent class.
0.04
Number of attributes divided by the number of instances.

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