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nomao_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

nomao_seed_3_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 nomao (1486) with seed=3 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
V1numeric15 unique values
0 missing
V2numeric28 unique values
0 missing
V3numeric590 unique values
0 missing
V4numeric307 unique values
0 missing
V5numeric355 unique values
0 missing
V6numeric409 unique values
0 missing
V8nominal2 unique values
0 missing
V9numeric5 unique values
0 missing
V10numeric7 unique values
0 missing
V12numeric32 unique values
0 missing
V13numeric51 unique values
0 missing
V14numeric36 unique values
0 missing
V15nominal3 unique values
0 missing
V16nominal3 unique values
0 missing
V17numeric4 unique values
0 missing
V18numeric4 unique values
0 missing
V19numeric14 unique values
0 missing
V20numeric15 unique values
0 missing
V21numeric17 unique values
0 missing
V22numeric16 unique values
0 missing
V23nominal3 unique values
0 missing
V24nominal3 unique values
0 missing
V25numeric17 unique values
0 missing
V26numeric25 unique values
0 missing
V27numeric258 unique values
0 missing
V28numeric165 unique values
0 missing
V30numeric203 unique values
0 missing
V31nominal3 unique values
0 missing
V32nominal3 unique values
0 missing
V34numeric24 unique values
0 missing
V36numeric50 unique values
0 missing
V37numeric56 unique values
0 missing
V38numeric57 unique values
0 missing
V39nominal3 unique values
0 missing
V40nominal3 unique values
0 missing
V41numeric3 unique values
0 missing
V42numeric3 unique values
0 missing
V44numeric12 unique values
0 missing
V45numeric14 unique values
0 missing
V46numeric12 unique values
0 missing
V47nominal3 unique values
0 missing
V48nominal3 unique values
0 missing
V49numeric5 unique values
0 missing
V50numeric5 unique values
0 missing
V51numeric43 unique values
0 missing
V54numeric23 unique values
0 missing
V55nominal3 unique values
0 missing
V56nominal3 unique values
0 missing
V57numeric35 unique values
0 missing
V58numeric61 unique values
0 missing
V60numeric694 unique values
0 missing
V61numeric623 unique values
0 missing
V62numeric766 unique values
0 missing
V64nominal3 unique values
0 missing
V65numeric31 unique values
0 missing
V67numeric373 unique values
0 missing
V68numeric273 unique values
0 missing
V69numeric379 unique values
0 missing
V70numeric437 unique values
0 missing
V71nominal2 unique values
0 missing
V72nominal2 unique values
0 missing
V74numeric4 unique values
0 missing
V75numeric15 unique values
0 missing
V76numeric15 unique values
0 missing
V77numeric14 unique values
0 missing
V78numeric15 unique values
0 missing
V79nominal3 unique values
0 missing
V80nominal3 unique values
0 missing
V81numeric2 unique values
0 missing
V83numeric2 unique values
0 missing
V84numeric2 unique values
0 missing
V86numeric2 unique values
0 missing
V87nominal2 unique values
0 missing
V88nominal2 unique values
0 missing
V89numeric112 unique values
0 missing
V90numeric26 unique values
0 missing
V93numeric17 unique values
0 missing
V94numeric14 unique values
0 missing
V95numeric14 unique values
0 missing
V96nominal3 unique values
0 missing
V97numeric87 unique values
0 missing
V98numeric12 unique values
0 missing
V99numeric16 unique values
0 missing
V100nominal3 unique values
0 missing
V101numeric737 unique values
0 missing
V102numeric23 unique values
0 missing
V103numeric44 unique values
0 missing
V104nominal2 unique values
0 missing
V105numeric711 unique values
0 missing
V106numeric20 unique values
0 missing
V107numeric47 unique values
0 missing
V108nominal2 unique values
0 missing
V110numeric47 unique values
0 missing
V111numeric68 unique values
0 missing
V112nominal3 unique values
0 missing
V113numeric557 unique values
0 missing
V115numeric72 unique values
0 missing
V116nominal3 unique values
0 missing
V117numeric343 unique values
0 missing
V118numeric231 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
74
Number of numeric attributes.
NumberOfSymbolicFeatures
27
Number of nominal attributes.
PercentageOfSymbolicFeatures
26.73
Percentage of nominal attributes.
AutoCorrelation
0.59
Average class difference between consecutive instances.
PercentageOfNumericFeatures
73.27
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
1.98
Percentage of binary attributes.
NumberOfBinaryFeatures
2
Number of binary attributes.
MinorityClassSize
571
Number of instances belonging to the least frequent class.
MinorityClassPercentage
28.55
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1429
Number of instances belonging to the most frequent class.
MajorityClassPercentage
71.45
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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