DEVELOPMENT... OpenML
Data
volkert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

volkert_seed_0_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 volkert (41166) 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, ) ```

101 features

class (target)nominal10 unique values
0 missing
V1numeric789 unique values
0 missing
V2numeric1 unique values
0 missing
V3numeric1 unique values
0 missing
V4numeric1 unique values
0 missing
V7numeric1 unique values
0 missing
V9numeric1 unique values
0 missing
V10numeric348 unique values
0 missing
V11numeric1 unique values
0 missing
V12numeric1 unique values
0 missing
V15numeric1 unique values
0 missing
V16numeric1 unique values
0 missing
V20numeric1 unique values
0 missing
V23numeric1 unique values
0 missing
V27numeric1 unique values
0 missing
V29numeric1 unique values
0 missing
V34numeric1 unique values
0 missing
V36numeric1 unique values
0 missing
V40numeric819 unique values
0 missing
V42numeric637 unique values
0 missing
V43numeric583 unique values
0 missing
V46numeric374 unique values
0 missing
V47numeric368 unique values
0 missing
V48numeric324 unique values
0 missing
V49numeric316 unique values
0 missing
V51numeric235 unique values
0 missing
V52numeric208 unique values
0 missing
V53numeric200 unique values
0 missing
V54numeric162 unique values
0 missing
V56numeric161 unique values
0 missing
V57numeric150 unique values
0 missing
V58numeric141 unique values
0 missing
V60numeric121 unique values
0 missing
V62numeric103 unique values
0 missing
V63numeric100 unique values
0 missing
V65numeric79 unique values
0 missing
V67numeric57 unique values
0 missing
V68numeric169 unique values
0 missing
V69numeric168 unique values
0 missing
V70numeric429 unique values
0 missing
V71numeric565 unique values
0 missing
V74numeric849 unique values
0 missing
V75numeric856 unique values
0 missing
V79numeric550 unique values
0 missing
V81numeric419 unique values
0 missing
V83numeric273 unique values
0 missing
V84numeric172 unique values
0 missing
V85numeric1863 unique values
0 missing
V86numeric1875 unique values
0 missing
V91numeric1895 unique values
0 missing
V92numeric1883 unique values
0 missing
V93numeric1609 unique values
0 missing
V94numeric1661 unique values
0 missing
V95numeric1577 unique values
0 missing
V98numeric1473 unique values
0 missing
V99numeric1481 unique values
0 missing
V101numeric1726 unique values
0 missing
V102numeric1751 unique values
0 missing
V107numeric1892 unique values
0 missing
V108numeric1896 unique values
0 missing
V109numeric415 unique values
0 missing
V110numeric283 unique values
0 missing
V111numeric296 unique values
0 missing
V112numeric298 unique values
0 missing
V119numeric352 unique values
0 missing
V122numeric262 unique values
0 missing
V123numeric259 unique values
0 missing
V124numeric265 unique values
0 missing
V125numeric272 unique values
0 missing
V126numeric365 unique values
0 missing
V127numeric342 unique values
0 missing
V129numeric255 unique values
0 missing
V130numeric265 unique values
0 missing
V132numeric268 unique values
0 missing
V133numeric284 unique values
0 missing
V134numeric352 unique values
0 missing
V135numeric297 unique values
0 missing
V136numeric275 unique values
0 missing
V139numeric324 unique values
0 missing
V141numeric334 unique values
0 missing
V143numeric379 unique values
0 missing
V146numeric387 unique values
0 missing
V148numeric345 unique values
0 missing
V150numeric328 unique values
0 missing
V151numeric319 unique values
0 missing
V154numeric292 unique values
0 missing
V156numeric278 unique values
0 missing
V158numeric270 unique values
0 missing
V159numeric259 unique values
0 missing
V160numeric260 unique values
0 missing
V161numeric273 unique values
0 missing
V162numeric352 unique values
0 missing
V163numeric327 unique values
0 missing
V164numeric263 unique values
0 missing
V165numeric267 unique values
0 missing
V166numeric265 unique values
0 missing
V167numeric262 unique values
0 missing
V168numeric268 unique values
0 missing
V170numeric355 unique values
0 missing
V171numeric278 unique values
0 missing
V175numeric283 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
10
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
0.14
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
Percentage of binary attributes.
NumberOfBinaryFeatures
0
Number of binary attributes.
MinorityClassSize
47
Number of instances belonging to the least frequent class.
MinorityClassPercentage
2.35
Percentage of instances belonging to the least frequent class.
MajorityClassSize
439
Number of instances belonging to the most frequent class.
MajorityClassPercentage
21.95
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