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ozone-level-8hr_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

ozone-level-8hr_seed_3_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 ozone-level-8hr (1487) 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, ) ```

73 features

Class (target)nominal2 unique values
0 missing
V54numeric101 unique values
0 missing
V38numeric317 unique values
0 missing
V53numeric244 unique values
0 missing
V52numeric288 unique values
0 missing
V51numeric311 unique values
0 missing
V50numeric279 unique values
0 missing
V49numeric281 unique values
0 missing
V48numeric288 unique values
0 missing
V47numeric294 unique values
0 missing
V46numeric295 unique values
0 missing
V45numeric308 unique values
0 missing
V44numeric316 unique values
0 missing
V43numeric327 unique values
0 missing
V42numeric326 unique values
0 missing
V41numeric325 unique values
0 missing
V40numeric326 unique values
0 missing
V39numeric323 unique values
0 missing
V37numeric314 unique values
0 missing
V64numeric101 unique values
0 missing
V72numeric157 unique values
0 missing
V71numeric56 unique values
0 missing
V70numeric69 unique values
0 missing
V69numeric609 unique values
0 missing
V68numeric948 unique values
0 missing
V67numeric85 unique values
0 missing
V66numeric1279 unique values
0 missing
V65numeric1427 unique values
0 missing
V55numeric1134 unique values
0 missing
V63numeric183 unique values
0 missing
V62numeric420 unique values
0 missing
V61numeric1238 unique values
0 missing
V60numeric1330 unique values
0 missing
V59numeric101 unique values
0 missing
V58numeric239 unique values
0 missing
V57numeric346 unique values
0 missing
V56numeric1265 unique values
0 missing
V10numeric70 unique values
0 missing
V18numeric72 unique values
0 missing
V17numeric72 unique values
0 missing
V16numeric78 unique values
0 missing
V15numeric77 unique values
0 missing
V14numeric79 unique values
0 missing
V13numeric78 unique values
0 missing
V12numeric78 unique values
0 missing
V11numeric77 unique values
0 missing
V19numeric67 unique values
0 missing
V9numeric70 unique values
0 missing
V8numeric67 unique values
0 missing
V7numeric66 unique values
0 missing
V6numeric62 unique values
0 missing
V5numeric63 unique values
0 missing
V4numeric64 unique values
0 missing
V3numeric66 unique values
0 missing
V2numeric68 unique values
0 missing
V28numeric278 unique values
0 missing
V36numeric303 unique values
0 missing
V35numeric309 unique values
0 missing
V34numeric308 unique values
0 missing
V33numeric287 unique values
0 missing
V32numeric283 unique values
0 missing
V31numeric277 unique values
0 missing
V30numeric279 unique values
0 missing
V29numeric284 unique values
0 missing
V1numeric69 unique values
0 missing
V27numeric275 unique values
0 missing
V26numeric54 unique values
0 missing
V25numeric74 unique values
0 missing
V24numeric66 unique values
0 missing
V23numeric68 unique values
0 missing
V22numeric69 unique values
0 missing
V21numeric69 unique values
0 missing
V20numeric65 unique values
0 missing
Show all 73 features

19 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
73
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
72
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfSymbolicFeatures
1.37
Percentage of nominal attributes.
AutoCorrelation
0.88
Average class difference between consecutive instances.
PercentageOfNumericFeatures
98.63
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
1.37
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
126
Number of instances belonging to the least frequent class.
MinorityClassPercentage
6.3
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1874
Number of instances belonging to the most frequent class.
MajorityClassPercentage
93.7
Percentage of instances belonging to the most frequent class.
Dimensionality
0.04
Number of attributes divided by the number of instances.
Show all 19 properties

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