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gina_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

gina_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 gina (41158) 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, ) ```

101 features

class (target)nominal2 unique values
0 missing
V19numeric229 unique values
0 missing
V25numeric112 unique values
0 missing
V31numeric218 unique values
0 missing
V38numeric227 unique values
0 missing
V53numeric225 unique values
0 missing
V57numeric237 unique values
0 missing
V60numeric224 unique values
0 missing
V77numeric185 unique values
0 missing
V85numeric32 unique values
0 missing
V108numeric221 unique values
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V109numeric186 unique values
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V112numeric93 unique values
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V113numeric227 unique values
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V122numeric243 unique values
0 missing
V127numeric229 unique values
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V144numeric236 unique values
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V150numeric118 unique values
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V185numeric152 unique values
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V206numeric132 unique values
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V220numeric44 unique values
0 missing
V228numeric231 unique values
0 missing
V239numeric116 unique values
0 missing
V242numeric235 unique values
0 missing
V250numeric233 unique values
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V257numeric234 unique values
0 missing
V271numeric198 unique values
0 missing
V273numeric102 unique values
0 missing
V274numeric27 unique values
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V275numeric83 unique values
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V296numeric214 unique values
0 missing
V300numeric66 unique values
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V331numeric170 unique values
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V341numeric237 unique values
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V347numeric237 unique values
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V350numeric241 unique values
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V363numeric241 unique values
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V366numeric216 unique values
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V374numeric236 unique values
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V393numeric60 unique values
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V398numeric192 unique values
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V403numeric223 unique values
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V407numeric231 unique values
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V410numeric189 unique values
0 missing
V413numeric229 unique values
0 missing
V427numeric66 unique values
0 missing
V435numeric124 unique values
0 missing
V436numeric228 unique values
0 missing
V445numeric172 unique values
0 missing
V447numeric34 unique values
0 missing
V456numeric42 unique values
0 missing
V457numeric239 unique values
0 missing
V481numeric99 unique values
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V482numeric206 unique values
0 missing
V489numeric227 unique values
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V491numeric43 unique values
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V493numeric187 unique values
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V501numeric149 unique values
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V529numeric150 unique values
0 missing
V559numeric239 unique values
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V567numeric238 unique values
0 missing
V572numeric216 unique values
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V577numeric66 unique values
0 missing
V584numeric32 unique values
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V611numeric228 unique values
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V649numeric239 unique values
0 missing
V660numeric217 unique values
0 missing
V669numeric224 unique values
0 missing
V673numeric127 unique values
0 missing
V680numeric34 unique values
0 missing
V685numeric236 unique values
0 missing
V689numeric57 unique values
0 missing
V709numeric196 unique values
0 missing
V722numeric232 unique values
0 missing
V728numeric102 unique values
0 missing
V729numeric108 unique values
0 missing
V732numeric42 unique values
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V734numeric35 unique values
0 missing
V749numeric225 unique values
0 missing
V766numeric198 unique values
0 missing
V770numeric225 unique values
0 missing
V772numeric96 unique values
0 missing
V796numeric148 unique values
0 missing
V807numeric210 unique values
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V814numeric66 unique values
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V820numeric160 unique values
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V826numeric57 unique values
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V831numeric231 unique values
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V833numeric243 unique values
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V856numeric230 unique values
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V865numeric231 unique values
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V883numeric142 unique values
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V885numeric231 unique values
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V895numeric38 unique values
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V901numeric151 unique values
0 missing
V903numeric91 unique values
0 missing
V926numeric31 unique values
0 missing
V927numeric235 unique values
0 missing
V946numeric238 unique values
0 missing
V947numeric23 unique values
0 missing
V966numeric44 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
0.51
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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