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albert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

albert_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 albert (41147) 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, ) ```

79 features

class (target)nominal2 unique values
0 missing
V59numeric149 unique values
69 missing
V41nominal4 unique values
0 missing
V58nominal812 unique values
0 missing
V57nominal680 unique values
0 missing
V56nominal1050 unique values
0 missing
V55nominal1403 unique values
0 missing
V54nominal250 unique values
0 missing
V53numeric17 unique values
1531 missing
V52numeric5 unique values
814 missing
V51numeric45 unique values
64 missing
V50numeric151 unique values
88 missing
V49nominal254 unique values
0 missing
V48nominal25 unique values
0 missing
V47nominal8 unique values
0 missing
V46nominal1197 unique values
0 missing
V45nominal9 unique values
0 missing
V44nominal1281 unique values
0 missing
V43numeric326 unique values
390 missing
V42numeric53 unique values
1 missing
V40numeric152 unique values
82 missing
V69numeric23 unique values
1495 missing
V78nominal814 unique values
0 missing
V77nominal879 unique values
0 missing
V76nominal30 unique values
0 missing
V75numeric47 unique values
72 missing
V74nominal34 unique values
0 missing
V73nominal36 unique values
0 missing
V72numeric41 unique values
78 missing
V71nominal31 unique values
0 missing
V70nominal29 unique values
0 missing
V60nominal261 unique values
0 missing
V68nominal19 unique values
0 missing
V67numeric24 unique values
1456 missing
V66nominal1175 unique values
0 missing
V65nominal20 unique values
0 missing
V64numeric26 unique values
1529 missing
V63nominal12 unique values
0 missing
V62nominal805 unique values
0 missing
V61nominal772 unique values
0 missing
V11numeric43 unique values
58 missing
V20nominal1165 unique values
0 missing
V19nominal8 unique values
0 missing
V18nominal34 unique values
0 missing
V17nominal1141 unique values
0 missing
V16nominal1415 unique values
0 missing
V15nominal240 unique values
0 missing
V14nominal81 unique values
0 missing
V13numeric53 unique values
440 missing
V12numeric22 unique values
1499 missing
V21nominal52 unique values
0 missing
V10numeric7 unique values
822 missing
V9numeric403 unique values
58 missing
V8numeric50 unique values
1 missing
V7numeric155 unique values
58 missing
V6numeric355 unique values
333 missing
V5numeric1442 unique values
37 missing
V4numeric46 unique values
440 missing
V3numeric126 unique values
441 missing
V2numeric345 unique values
0 missing
V30nominal9 unique values
0 missing
V39nominal676 unique values
0 missing
V38nominal32 unique values
0 missing
V37nominal803 unique values
0 missing
V36nominal12 unique values
0 missing
V35nominal9 unique values
0 missing
V34nominal1314 unique values
0 missing
V33nominal4 unique values
0 missing
V32nominal221 unique values
0 missing
V31nominal558 unique values
0 missing
V1numeric46 unique values
822 missing
V29nominal1267 unique values
0 missing
V28nominal868 unique values
0 missing
V27nominal20 unique values
0 missing
V26nominal811 unique values
0 missing
V25nominal1342 unique values
0 missing
V24nominal915 unique values
0 missing
V23nominal1040 unique values
0 missing
V22nominal2 unique values
0 missing
Show all 79 features

19 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
79
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
12678
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
2000
Number of instances with at least one value missing.
NumberOfNumericFeatures
26
Number of numeric attributes.
NumberOfSymbolicFeatures
53
Number of nominal attributes.
PercentageOfSymbolicFeatures
67.09
Percentage of nominal attributes.
AutoCorrelation
0.52
Average class difference between consecutive instances.
PercentageOfNumericFeatures
32.91
Percentage of numeric attributes.
PercentageOfMissingValues
8.02
Percentage of missing values.
PercentageOfInstancesWithMissingValues
100
Percentage of instances having missing values.
PercentageOfBinaryFeatures
1.27
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
1000
Number of instances belonging to the least frequent class.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1000
Number of instances belonging to the most frequent class.
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
Dimensionality
0.04
Number of attributes divided by the number of instances.
Show all 19 properties

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