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fabert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

fabert_seed_0_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 fabert (41164) 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)nominal7 unique values
0 missing
V2numeric2 unique values
0 missing
V4numeric6 unique values
0 missing
V7numeric37 unique values
0 missing
V12numeric16 unique values
0 missing
V17numeric22 unique values
0 missing
V22numeric46 unique values
0 missing
V25numeric12 unique values
0 missing
V29numeric12 unique values
0 missing
V39numeric4 unique values
0 missing
V54numeric30 unique values
0 missing
V57numeric22 unique values
0 missing
V60numeric7 unique values
0 missing
V66numeric12 unique values
0 missing
V68numeric7 unique values
0 missing
V93numeric25 unique values
0 missing
V104numeric13 unique values
0 missing
V125numeric21 unique values
0 missing
V129numeric17 unique values
0 missing
V181numeric44 unique values
0 missing
V190numeric50 unique values
0 missing
V193numeric2 unique values
0 missing
V200numeric20 unique values
0 missing
V210numeric22 unique values
0 missing
V218numeric11 unique values
0 missing
V222numeric3 unique values
0 missing
V240numeric5 unique values
0 missing
V253numeric2 unique values
0 missing
V260numeric41 unique values
0 missing
V267numeric5 unique values
0 missing
V280numeric7 unique values
0 missing
V286numeric3 unique values
0 missing
V288numeric10 unique values
0 missing
V289numeric14 unique values
0 missing
V290numeric6 unique values
0 missing
V298numeric6 unique values
0 missing
V299numeric48 unique values
0 missing
V302numeric60 unique values
0 missing
V310numeric44 unique values
0 missing
V313numeric13 unique values
0 missing
V327numeric46 unique values
0 missing
V348numeric11 unique values
0 missing
V356numeric3 unique values
0 missing
V360numeric25 unique values
0 missing
V377numeric1 unique values
0 missing
V391numeric36 unique values
0 missing
V393numeric49 unique values
0 missing
V397numeric10 unique values
0 missing
V399numeric8 unique values
0 missing
V403numeric6 unique values
0 missing
V405numeric35 unique values
0 missing
V414numeric37 unique values
0 missing
V434numeric30 unique values
0 missing
V445numeric59 unique values
0 missing
V447numeric10 unique values
0 missing
V448numeric16 unique values
0 missing
V454numeric6 unique values
0 missing
V462numeric12 unique values
0 missing
V468numeric24 unique values
0 missing
V485numeric24 unique values
0 missing
V486numeric13 unique values
0 missing
V495numeric8 unique values
0 missing
V497numeric9 unique values
0 missing
V501numeric10 unique values
0 missing
V520numeric42 unique values
0 missing
V523numeric32 unique values
0 missing
V525numeric27 unique values
0 missing
V526numeric3 unique values
0 missing
V533numeric4 unique values
0 missing
V538numeric45 unique values
0 missing
V555numeric20 unique values
0 missing
V558numeric10 unique values
0 missing
V565numeric41 unique values
0 missing
V568numeric19 unique values
0 missing
V574numeric16 unique values
0 missing
V578numeric65 unique values
0 missing
V590numeric36 unique values
0 missing
V597numeric11 unique values
0 missing
V601numeric51 unique values
0 missing
V608numeric24 unique values
0 missing
V619numeric30 unique values
0 missing
V623numeric56 unique values
0 missing
V634numeric19 unique values
0 missing
V640numeric21 unique values
0 missing
V650numeric5 unique values
0 missing
V651numeric13 unique values
0 missing
V667numeric21 unique values
0 missing
V670numeric9 unique values
0 missing
V674numeric6 unique values
0 missing
V691numeric3 unique values
0 missing
V695numeric31 unique values
0 missing
V706numeric7 unique values
0 missing
V713numeric2 unique values
0 missing
V722numeric5 unique values
0 missing
V727numeric7 unique values
0 missing
V742numeric24 unique values
0 missing
V750numeric37 unique values
0 missing
V752numeric48 unique values
0 missing
V775numeric28 unique values
0 missing
V778numeric33 unique values
0 missing
V783numeric16 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
7
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
0.16
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
122
Number of instances belonging to the least frequent class.
MinorityClassPercentage
6.1
Percentage of instances belonging to the least frequent class.
MajorityClassSize
468
Number of instances belonging to the most frequent class.
MajorityClassPercentage
23.4
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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