DEVELOPMENT... OpenML
Data
gina_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

gina_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Subsampling of the dataset gina (41158) 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
V26numeric231 unique values
0 missing
V34numeric82 unique values
0 missing
V55numeric195 unique values
0 missing
V71numeric154 unique values
0 missing
V75numeric227 unique values
0 missing
V83numeric126 unique values
0 missing
V110numeric221 unique values
0 missing
V123numeric233 unique values
0 missing
V124numeric41 unique values
0 missing
V126numeric183 unique values
0 missing
V154numeric239 unique values
0 missing
V155numeric219 unique values
0 missing
V162numeric235 unique values
0 missing
V165numeric33 unique values
0 missing
V172numeric111 unique values
0 missing
V183numeric125 unique values
0 missing
V187numeric229 unique values
0 missing
V191numeric238 unique values
0 missing
V196numeric62 unique values
0 missing
V207numeric127 unique values
0 missing
V209numeric103 unique values
0 missing
V214numeric240 unique values
0 missing
V250numeric233 unique values
0 missing
V252numeric237 unique values
0 missing
V277numeric118 unique values
0 missing
V302numeric21 unique values
0 missing
V325numeric241 unique values
0 missing
V333numeric176 unique values
0 missing
V334numeric26 unique values
0 missing
V337numeric28 unique values
0 missing
V348numeric107 unique values
0 missing
V349numeric184 unique values
0 missing
V371numeric141 unique values
0 missing
V373numeric193 unique values
0 missing
V386numeric90 unique values
0 missing
V399numeric242 unique values
0 missing
V422numeric209 unique values
0 missing
V427numeric59 unique values
0 missing
V441numeric45 unique values
0 missing
V447numeric35 unique values
0 missing
V452numeric62 unique values
0 missing
V456numeric38 unique values
0 missing
V459numeric20 unique values
0 missing
V460numeric136 unique values
0 missing
V463numeric24 unique values
0 missing
V465numeric126 unique values
0 missing
V471numeric195 unique values
0 missing
V480numeric220 unique values
0 missing
V485numeric235 unique values
0 missing
V487numeric128 unique values
0 missing
V501numeric141 unique values
0 missing
V514numeric230 unique values
0 missing
V515numeric107 unique values
0 missing
V520numeric154 unique values
0 missing
V535numeric240 unique values
0 missing
V538numeric179 unique values
0 missing
V546numeric32 unique values
0 missing
V554numeric36 unique values
0 missing
V555numeric48 unique values
0 missing
V599numeric224 unique values
0 missing
V600numeric155 unique values
0 missing
V610numeric72 unique values
0 missing
V633numeric236 unique values
0 missing
V634numeric238 unique values
0 missing
V640numeric234 unique values
0 missing
V643numeric98 unique values
0 missing
V660numeric227 unique values
0 missing
V662numeric47 unique values
0 missing
V709numeric195 unique values
0 missing
V736numeric207 unique values
0 missing
V770numeric218 unique values
0 missing
V771numeric211 unique values
0 missing
V775numeric131 unique values
0 missing
V777numeric176 unique values
0 missing
V790numeric54 unique values
0 missing
V804numeric94 unique values
0 missing
V805numeric87 unique values
0 missing
V820numeric166 unique values
0 missing
V823numeric234 unique values
0 missing
V838numeric180 unique values
0 missing
V841numeric28 unique values
0 missing
V842numeric181 unique values
0 missing
V851numeric238 unique values
0 missing
V856numeric223 unique values
0 missing
V862numeric71 unique values
0 missing
V868numeric149 unique values
0 missing
V875numeric128 unique values
0 missing
V876numeric63 unique values
0 missing
V880numeric206 unique values
0 missing
V886numeric223 unique values
0 missing
V888numeric231 unique values
0 missing
V898numeric19 unique values
0 missing
V907numeric222 unique values
0 missing
V930numeric204 unique values
0 missing
V936numeric239 unique values
0 missing
V938numeric237 unique values
0 missing
V942numeric188 unique values
0 missing
V959numeric82 unique values
0 missing
V960numeric57 unique values
0 missing
V968numeric76 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

0 tasks

Define a new task