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christine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

christine_seed_4_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 christine (41142) 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, ) ```

101 features

class (target)nominal2 unique values
0 missing
V44numeric208 unique values
0 missing
V57numeric442 unique values
0 missing
V95numeric552 unique values
0 missing
V125numeric24 unique values
0 missing
V128numeric510 unique values
0 missing
V143numeric337 unique values
0 missing
V187numeric394 unique values
0 missing
V209numeric506 unique values
0 missing
V212numeric392 unique values
0 missing
V218numeric355 unique values
0 missing
V264numeric494 unique values
0 missing
V271numeric97 unique values
0 missing
V278numeric390 unique values
0 missing
V280numeric249 unique values
0 missing
V291numeric332 unique values
0 missing
V310numeric238 unique values
0 missing
V321numeric184 unique values
0 missing
V329numeric443 unique values
0 missing
V342numeric336 unique values
0 missing
V351numeric272 unique values
0 missing
V358numeric391 unique values
0 missing
V361numeric209 unique values
0 missing
V431numeric517 unique values
0 missing
V439numeric437 unique values
0 missing
V480numeric132 unique values
0 missing
V527numeric402 unique values
0 missing
V558numeric437 unique values
0 missing
V580numeric282 unique values
0 missing
V583numeric343 unique values
0 missing
V589numeric328 unique values
0 missing
V594numeric293 unique values
0 missing
V626numeric463 unique values
0 missing
V645numeric356 unique values
0 missing
V673numeric337 unique values
0 missing
V701numeric176 unique values
0 missing
V731numeric435 unique values
0 missing
V745numeric247 unique values
0 missing
V747numeric281 unique values
0 missing
V769numeric148 unique values
0 missing
V779numeric396 unique values
0 missing
V784numeric332 unique values
0 missing
V788numeric506 unique values
0 missing
V790numeric325 unique values
0 missing
V792numeric382 unique values
0 missing
V796numeric531 unique values
0 missing
V803numeric296 unique values
0 missing
V827numeric279 unique values
0 missing
V835numeric322 unique values
0 missing
V847numeric210 unique values
0 missing
V857nominal1 unique values
0 missing
V873numeric383 unique values
0 missing
V901numeric439 unique values
0 missing
V921numeric272 unique values
0 missing
V928numeric250 unique values
0 missing
V934numeric435 unique values
0 missing
V939numeric338 unique values
0 missing
V960numeric345 unique values
0 missing
V972numeric555 unique values
0 missing
V1026numeric354 unique values
0 missing
V1050numeric118 unique values
0 missing
V1054numeric220 unique values
0 missing
V1074numeric349 unique values
0 missing
V1090numeric406 unique values
0 missing
V1113numeric398 unique values
0 missing
V1117numeric100 unique values
0 missing
V1121numeric517 unique values
0 missing
V1150numeric279 unique values
0 missing
V1234numeric230 unique values
0 missing
V1243numeric443 unique values
0 missing
V1249numeric100 unique values
0 missing
V1310numeric406 unique values
0 missing
V1313numeric399 unique values
0 missing
V1355numeric267 unique values
0 missing
V1357numeric376 unique values
0 missing
V1360numeric290 unique values
0 missing
V1402numeric436 unique values
0 missing
V1406numeric416 unique values
0 missing
V1408numeric417 unique values
0 missing
V1440numeric372 unique values
0 missing
V1445numeric626 unique values
0 missing
V1450numeric151 unique values
0 missing
V1451numeric542 unique values
0 missing
V1458numeric288 unique values
0 missing
V1461numeric481 unique values
0 missing
V1490numeric441 unique values
0 missing
V1496numeric423 unique values
0 missing
V1498numeric361 unique values
0 missing
V1505numeric421 unique values
0 missing
V1506numeric515 unique values
0 missing
V1510numeric318 unique values
0 missing
V1522numeric420 unique values
0 missing
V1527numeric187 unique values
0 missing
V1542numeric295 unique values
0 missing
V1571numeric458 unique values
0 missing
V1585numeric216 unique values
0 missing
V1591numeric539 unique values
0 missing
V1602numeric237 unique values
0 missing
V1610numeric107 unique values
0 missing
V1623numeric194 unique values
0 missing
V1632numeric272 unique values
0 missing
Show first 100 features

19 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
101
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
99
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
PercentageOfSymbolicFeatures
1.98
Percentage of nominal attributes.
AutoCorrelation
0.48
Average class difference between consecutive instances.
PercentageOfNumericFeatures
98.02
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
0.99
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
1000
Number of instances belonging to the least frequent class.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1000
Number of instances belonging to the most frequent class.
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
Dimensionality
0.05
Number of attributes divided by the number of instances.
Show all 19 properties

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