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albert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

albert_seed_1_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=1 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
V59numeric139 unique values
77 missing
V41nominal4 unique values
0 missing
V58nominal828 unique values
0 missing
V57nominal656 unique values
0 missing
V56nominal1069 unique values
0 missing
V55nominal1432 unique values
0 missing
V54nominal249 unique values
0 missing
V53numeric20 unique values
1535 missing
V52numeric5 unique values
835 missing
V51numeric47 unique values
76 missing
V50numeric136 unique values
73 missing
V49nominal250 unique values
0 missing
V48nominal32 unique values
0 missing
V47nominal7 unique values
0 missing
V46nominal1162 unique values
0 missing
V45nominal9 unique values
0 missing
V44nominal1264 unique values
0 missing
V43numeric343 unique values
346 missing
V42numeric50 unique values
0 missing
V40numeric152 unique values
75 missing
V69numeric27 unique values
1518 missing
V78nominal817 unique values
0 missing
V77nominal881 unique values
0 missing
V76nominal32 unique values
0 missing
V75numeric42 unique values
62 missing
V74nominal34 unique values
0 missing
V73nominal33 unique values
0 missing
V72numeric50 unique values
71 missing
V71nominal28 unique values
0 missing
V70nominal31 unique values
0 missing
V60nominal254 unique values
0 missing
V68nominal19 unique values
0 missing
V67numeric18 unique values
1520 missing
V66nominal1167 unique values
0 missing
V65nominal19 unique values
0 missing
V64numeric22 unique values
1538 missing
V63nominal13 unique values
0 missing
V62nominal818 unique values
0 missing
V61nominal802 unique values
0 missing
V11numeric42 unique values
84 missing
V20nominal1181 unique values
0 missing
V19nominal7 unique values
0 missing
V18nominal35 unique values
0 missing
V17nominal1149 unique values
0 missing
V16nominal1435 unique values
0 missing
V15nominal258 unique values
0 missing
V14nominal78 unique values
0 missing
V13numeric58 unique values
433 missing
V12numeric23 unique values
1495 missing
V21nominal49 unique values
0 missing
V10numeric5 unique values
808 missing
V9numeric407 unique values
84 missing
V8numeric50 unique values
1 missing
V7numeric146 unique values
84 missing
V6numeric345 unique values
369 missing
V5numeric1410 unique values
48 missing
V4numeric48 unique values
433 missing
V3numeric139 unique values
453 missing
V2numeric342 unique values
0 missing
V30nominal9 unique values
0 missing
V39nominal645 unique values
0 missing
V38nominal31 unique values
0 missing
V37nominal791 unique values
0 missing
V36nominal12 unique values
0 missing
V35nominal7 unique values
0 missing
V34nominal1333 unique values
0 missing
V33nominal4 unique values
0 missing
V32nominal186 unique values
0 missing
V31nominal590 unique values
0 missing
V1numeric42 unique values
808 missing
V29nominal1282 unique values
0 missing
V28nominal912 unique values
0 missing
V27nominal19 unique values
0 missing
V26nominal813 unique values
0 missing
V25nominal1375 unique values
0 missing
V24nominal910 unique values
0 missing
V23nominal1076 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
12826
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
0.5
Average class difference between consecutive instances.
PercentageOfNumericFeatures
32.91
Percentage of numeric attributes.
PercentageOfMissingValues
8.12
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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