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ozone-level-8hr_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

ozone-level-8hr_seed_0_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=0 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
V53numeric242 unique values
0 missing
V52numeric288 unique values
0 missing
V51numeric316 unique values
0 missing
V50numeric272 unique values
0 missing
V49numeric278 unique values
0 missing
V48numeric286 unique values
0 missing
V47numeric292 unique values
0 missing
V46numeric296 unique values
0 missing
V45numeric302 unique values
0 missing
V44numeric320 unique values
0 missing
V43numeric321 unique values
0 missing
V42numeric321 unique values
0 missing
V41numeric320 unique values
0 missing
V40numeric319 unique values
0 missing
V39numeric321 unique values
0 missing
V37numeric320 unique values
0 missing
V64numeric101 unique values
0 missing
V72numeric160 unique values
0 missing
V71numeric56 unique values
0 missing
V70numeric71 unique values
0 missing
V69numeric625 unique values
0 missing
V68numeric941 unique values
0 missing
V67numeric83 unique values
0 missing
V66numeric1289 unique values
0 missing
V65numeric1411 unique values
0 missing
V55numeric1124 unique values
0 missing
V63numeric181 unique values
0 missing
V62numeric421 unique values
0 missing
V61numeric1236 unique values
0 missing
V60numeric1333 unique values
0 missing
V59numeric101 unique values
0 missing
V58numeric231 unique values
0 missing
V57numeric347 unique values
0 missing
V56numeric1272 unique values
0 missing
V10numeric69 unique values
0 missing
V18numeric72 unique values
0 missing
V17numeric71 unique values
0 missing
V16numeric78 unique values
0 missing
V15numeric76 unique values
0 missing
V14numeric77 unique values
0 missing
V13numeric75 unique values
0 missing
V12numeric77 unique values
0 missing
V11numeric75 unique values
0 missing
V19numeric69 unique values
0 missing
V9numeric70 unique values
0 missing
V8numeric67 unique values
0 missing
V7numeric66 unique values
0 missing
V6numeric62 unique values
0 missing
V5numeric62 unique values
0 missing
V4numeric66 unique values
0 missing
V3numeric63 unique values
0 missing
V2numeric69 unique values
0 missing
V28numeric277 unique values
0 missing
V36numeric306 unique values
0 missing
V35numeric308 unique values
0 missing
V34numeric306 unique values
0 missing
V33numeric293 unique values
0 missing
V32numeric286 unique values
0 missing
V31numeric278 unique values
0 missing
V30numeric279 unique values
0 missing
V29numeric281 unique values
0 missing
V1numeric68 unique values
0 missing
V27numeric277 unique values
0 missing
V26numeric55 unique values
0 missing
V25numeric75 unique values
0 missing
V24numeric65 unique values
0 missing
V23numeric67 unique values
0 missing
V22numeric69 unique values
0 missing
V21numeric67 unique values
0 missing
V20numeric63 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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