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dilbert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

dilbert_seed_2_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 dilbert (41163) with seed=2 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)nominal5 unique values
0 missing
V78numeric1999 unique values
0 missing
V106numeric2000 unique values
0 missing
V114numeric1997 unique values
0 missing
V152numeric1996 unique values
0 missing
V176numeric1994 unique values
0 missing
V193numeric1999 unique values
0 missing
V202numeric1999 unique values
0 missing
V205numeric1997 unique values
0 missing
V209numeric1998 unique values
0 missing
V210numeric1995 unique values
0 missing
V289numeric1999 unique values
0 missing
V360numeric1996 unique values
0 missing
V364numeric1998 unique values
0 missing
V387numeric1996 unique values
0 missing
V397numeric1997 unique values
0 missing
V422numeric1996 unique values
0 missing
V425numeric1997 unique values
0 missing
V432numeric1996 unique values
0 missing
V438numeric1999 unique values
0 missing
V498numeric1999 unique values
0 missing
V501numeric1999 unique values
0 missing
V517numeric2000 unique values
0 missing
V528numeric1997 unique values
0 missing
V569numeric2000 unique values
0 missing
V588numeric1997 unique values
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V621numeric1998 unique values
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V640numeric1998 unique values
0 missing
V647numeric2000 unique values
0 missing
V672numeric1997 unique values
0 missing
V676numeric1999 unique values
0 missing
V737numeric2000 unique values
0 missing
V759numeric1997 unique values
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V789numeric1996 unique values
0 missing
V802numeric1998 unique values
0 missing
V816numeric1995 unique values
0 missing
V834numeric1999 unique values
0 missing
V861numeric1995 unique values
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V868numeric1994 unique values
0 missing
V874numeric1991 unique values
0 missing
V882numeric1993 unique values
0 missing
V883numeric1996 unique values
0 missing
V895numeric1996 unique values
0 missing
V921numeric1995 unique values
0 missing
V933numeric1994 unique values
0 missing
V935numeric1992 unique values
0 missing
V964numeric1992 unique values
0 missing
V983numeric1996 unique values
0 missing
V991numeric1989 unique values
0 missing
V994numeric1990 unique values
0 missing
V1002numeric1996 unique values
0 missing
V1020numeric1996 unique values
0 missing
V1027numeric1992 unique values
0 missing
V1043numeric1992 unique values
0 missing
V1071numeric1996 unique values
0 missing
V1081numeric1996 unique values
0 missing
V1114numeric1995 unique values
0 missing
V1127numeric1994 unique values
0 missing
V1147numeric1997 unique values
0 missing
V1170numeric1994 unique values
0 missing
V1172numeric1998 unique values
0 missing
V1173numeric1995 unique values
0 missing
V1218numeric1995 unique values
0 missing
V1224numeric1996 unique values
0 missing
V1261numeric2000 unique values
0 missing
V1263numeric1997 unique values
0 missing
V1270numeric1997 unique values
0 missing
V1291numeric1998 unique values
0 missing
V1308numeric1996 unique values
0 missing
V1323numeric1994 unique values
0 missing
V1337numeric1994 unique values
0 missing
V1347numeric1994 unique values
0 missing
V1357numeric1997 unique values
0 missing
V1371numeric1994 unique values
0 missing
V1379numeric1996 unique values
0 missing
V1394numeric1992 unique values
0 missing
V1443numeric1996 unique values
0 missing
V1511numeric1992 unique values
0 missing
V1533numeric1994 unique values
0 missing
V1551numeric1998 unique values
0 missing
V1552numeric1992 unique values
0 missing
V1555numeric1996 unique values
0 missing
V1593numeric1995 unique values
0 missing
V1648numeric1991 unique values
0 missing
V1672numeric1989 unique values
0 missing
V1680numeric1992 unique values
0 missing
V1686numeric1995 unique values
0 missing
V1706numeric1993 unique values
0 missing
V1708numeric1992 unique values
0 missing
V1735numeric1995 unique values
0 missing
V1738numeric1996 unique values
0 missing
V1769numeric1998 unique values
0 missing
V1771numeric1998 unique values
0 missing
V1805numeric1999 unique values
0 missing
V1808numeric1997 unique values
0 missing
V1824numeric1997 unique values
0 missing
V1872numeric1996 unique values
0 missing
V1879numeric1997 unique values
0 missing
V1900numeric1999 unique values
0 missing
V1904numeric1999 unique values
0 missing
V2000numeric1998 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
5
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
0.2
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
382
Number of instances belonging to the least frequent class.
MinorityClassPercentage
19.1
Percentage of instances belonging to the least frequent class.
MajorityClassSize
410
Number of instances belonging to the most frequent class.
MajorityClassPercentage
20.5
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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