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fabert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

fabert_seed_4_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=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)nominal7 unique values
0 missing
V21numeric1 unique values
0 missing
V28numeric28 unique values
0 missing
V44numeric30 unique values
0 missing
V58numeric82 unique values
0 missing
V61numeric61 unique values
0 missing
V68numeric12 unique values
0 missing
V90numeric9 unique values
0 missing
V100numeric7 unique values
0 missing
V102numeric27 unique values
0 missing
V125numeric22 unique values
0 missing
V126numeric18 unique values
0 missing
V132numeric11 unique values
0 missing
V136numeric21 unique values
0 missing
V141numeric12 unique values
0 missing
V150numeric25 unique values
0 missing
V153numeric30 unique values
0 missing
V155numeric47 unique values
0 missing
V158numeric37 unique values
0 missing
V170numeric9 unique values
0 missing
V171numeric3 unique values
0 missing
V176numeric15 unique values
0 missing
V202numeric7 unique values
0 missing
V207numeric22 unique values
0 missing
V226numeric36 unique values
0 missing
V244numeric9 unique values
0 missing
V266numeric34 unique values
0 missing
V269numeric6 unique values
0 missing
V271numeric32 unique values
0 missing
V275numeric5 unique values
0 missing
V286numeric4 unique values
0 missing
V287numeric11 unique values
0 missing
V304numeric37 unique values
0 missing
V306numeric16 unique values
0 missing
V313numeric18 unique values
0 missing
V322numeric34 unique values
0 missing
V343numeric23 unique values
0 missing
V346numeric7 unique values
0 missing
V360numeric20 unique values
0 missing
V363numeric15 unique values
0 missing
V371numeric11 unique values
0 missing
V372numeric14 unique values
0 missing
V374numeric62 unique values
0 missing
V375numeric6 unique values
0 missing
V378numeric11 unique values
0 missing
V381numeric4 unique values
0 missing
V386numeric4 unique values
0 missing
V391numeric36 unique values
0 missing
V392numeric13 unique values
0 missing
V398numeric1 unique values
0 missing
V410numeric14 unique values
0 missing
V416numeric33 unique values
0 missing
V422numeric11 unique values
0 missing
V423numeric9 unique values
0 missing
V432numeric11 unique values
0 missing
V435numeric3 unique values
0 missing
V440numeric4 unique values
0 missing
V447numeric15 unique values
0 missing
V448numeric18 unique values
0 missing
V451numeric11 unique values
0 missing
V485numeric22 unique values
0 missing
V490numeric43 unique values
0 missing
V497numeric13 unique values
0 missing
V510numeric45 unique values
0 missing
V521numeric14 unique values
0 missing
V524numeric17 unique values
0 missing
V525numeric36 unique values
0 missing
V538numeric33 unique values
0 missing
V542numeric3 unique values
0 missing
V573numeric12 unique values
0 missing
V575numeric20 unique values
0 missing
V605numeric10 unique values
0 missing
V620numeric4 unique values
0 missing
V626numeric14 unique values
0 missing
V633numeric16 unique values
0 missing
V640numeric31 unique values
0 missing
V645numeric11 unique values
0 missing
V651numeric12 unique values
0 missing
V652numeric20 unique values
0 missing
V663numeric15 unique values
0 missing
V664numeric59 unique values
0 missing
V670numeric11 unique values
0 missing
V680numeric20 unique values
0 missing
V684numeric10 unique values
0 missing
V686numeric7 unique values
0 missing
V688numeric7 unique values
0 missing
V690numeric5 unique values
0 missing
V702numeric9 unique values
0 missing
V704numeric9 unique values
0 missing
V706numeric7 unique values
0 missing
V712numeric13 unique values
0 missing
V718numeric8 unique values
0 missing
V719numeric3 unique values
0 missing
V720numeric5 unique values
0 missing
V725numeric16 unique values
0 missing
V767numeric26 unique values
0 missing
V769numeric52 unique values
0 missing
V771numeric39 unique values
0 missing
V789numeric3 unique values
0 missing
V790numeric20 unique values
0 missing
V797numeric7 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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