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gina_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

gina_seed_2_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 gina (41158) with seed=2 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
V37numeric38 unique values
0 missing
V38numeric221 unique values
0 missing
V49numeric44 unique values
0 missing
V55numeric186 unique values
0 missing
V74numeric225 unique values
0 missing
V81numeric232 unique values
0 missing
V93numeric195 unique values
0 missing
V96numeric219 unique values
0 missing
V98numeric40 unique values
0 missing
V100numeric189 unique values
0 missing
V135numeric180 unique values
0 missing
V167numeric231 unique values
0 missing
V171numeric97 unique values
0 missing
V180numeric43 unique values
0 missing
V189numeric206 unique values
0 missing
V198numeric233 unique values
0 missing
V203numeric181 unique values
0 missing
V207numeric138 unique values
0 missing
V209numeric97 unique values
0 missing
V229numeric231 unique values
0 missing
V233numeric235 unique values
0 missing
V245numeric45 unique values
0 missing
V251numeric223 unique values
0 missing
V261numeric195 unique values
0 missing
V273numeric100 unique values
0 missing
V293numeric208 unique values
0 missing
V295numeric207 unique values
0 missing
V304numeric127 unique values
0 missing
V316numeric240 unique values
0 missing
V327numeric235 unique values
0 missing
V345numeric237 unique values
0 missing
V356numeric238 unique values
0 missing
V363numeric240 unique values
0 missing
V381numeric238 unique values
0 missing
V383numeric234 unique values
0 missing
V388numeric106 unique values
0 missing
V396numeric225 unique values
0 missing
V417numeric197 unique values
0 missing
V420numeric167 unique values
0 missing
V426numeric215 unique values
0 missing
V428numeric175 unique values
0 missing
V436numeric222 unique values
0 missing
V444numeric195 unique values
0 missing
V447numeric34 unique values
0 missing
V463numeric18 unique values
0 missing
V468numeric208 unique values
0 missing
V478numeric232 unique values
0 missing
V486numeric104 unique values
0 missing
V488numeric161 unique values
0 missing
V489numeric226 unique values
0 missing
V496numeric215 unique values
0 missing
V502numeric160 unique values
0 missing
V505numeric94 unique values
0 missing
V527numeric236 unique values
0 missing
V529numeric155 unique values
0 missing
V530numeric199 unique values
0 missing
V558numeric190 unique values
0 missing
V560numeric228 unique values
0 missing
V562numeric238 unique values
0 missing
V572numeric216 unique values
0 missing
V586numeric227 unique values
0 missing
V602numeric124 unique values
0 missing
V606numeric227 unique values
0 missing
V609numeric89 unique values
0 missing
V619numeric50 unique values
0 missing
V634numeric236 unique values
0 missing
V637numeric228 unique values
0 missing
V643numeric97 unique values
0 missing
V650numeric205 unique values
0 missing
V665numeric59 unique values
0 missing
V671numeric239 unique values
0 missing
V712numeric228 unique values
0 missing
V714numeric209 unique values
0 missing
V717numeric194 unique values
0 missing
V730numeric62 unique values
0 missing
V741numeric106 unique values
0 missing
V742numeric209 unique values
0 missing
V780numeric204 unique values
0 missing
V786numeric221 unique values
0 missing
V793numeric209 unique values
0 missing
V797numeric184 unique values
0 missing
V805numeric91 unique values
0 missing
V817numeric225 unique values
0 missing
V818numeric188 unique values
0 missing
V827numeric160 unique values
0 missing
V836numeric117 unique values
0 missing
V845numeric86 unique values
0 missing
V850numeric155 unique values
0 missing
V853numeric231 unique values
0 missing
V875numeric126 unique values
0 missing
V877numeric239 unique values
0 missing
V892numeric128 unique values
0 missing
V898numeric24 unique values
0 missing
V926numeric29 unique values
0 missing
V937numeric187 unique values
0 missing
V952numeric112 unique values
0 missing
V953numeric81 unique values
0 missing
V954numeric227 unique values
0 missing
V961numeric231 unique values
0 missing
V968numeric65 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
100
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfSymbolicFeatures
0.99
Percentage of nominal attributes.
AutoCorrelation
0.48
Average class difference between consecutive instances.
PercentageOfNumericFeatures
99.01
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
983
Number of instances belonging to the least frequent class.
MinorityClassPercentage
49.15
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1017
Number of instances belonging to the most frequent class.
MajorityClassPercentage
50.85
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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