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Bioresponse_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

Bioresponse_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 Bioresponse (4134) 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

target (target)nominal2 unique values
0 missing
D48numeric512 unique values
0 missing
D62numeric116 unique values
0 missing
D103numeric1743 unique values
0 missing
D137numeric13 unique values
0 missing
D139numeric26 unique values
0 missing
D156numeric6 unique values
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D203numeric366 unique values
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D227numeric7 unique values
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D238numeric5 unique values
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D287numeric3 unique values
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D296numeric8 unique values
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D302numeric2 unique values
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D305numeric4 unique values
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D317numeric3 unique values
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D337numeric10 unique values
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D349numeric9 unique values
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D358numeric4 unique values
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D373numeric9 unique values
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D382numeric8 unique values
0 missing
D389numeric5 unique values
0 missing
D392numeric6 unique values
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D468numeric7 unique values
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D478numeric8 unique values
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D522numeric8 unique values
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D574numeric4 unique values
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D607numeric10 unique values
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D632numeric3 unique values
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D639numeric4 unique values
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D646numeric4 unique values
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D702numeric10 unique values
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D873numeric8 unique values
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D900numeric6 unique values
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D908numeric5 unique values
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D923numeric3 unique values
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D932numeric5 unique values
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D948numeric6 unique values
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D982numeric2 unique values
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D983numeric2 unique values
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D1002numeric2 unique values
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D1016numeric2 unique values
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D1764numeric2 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
915
Number of instances belonging to the least frequent class.
MinorityClassPercentage
45.75
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1085
Number of instances belonging to the most frequent class.
MajorityClassPercentage
54.25
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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