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albert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

albert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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Subsampling of the dataset albert (41147) with seed=0 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
V1numeric53 unique values
873 missing
V2numeric370 unique values
0 missing
V3numeric119 unique values
493 missing
V4numeric50 unique values
462 missing
V5numeric1443 unique values
42 missing
V6numeric333 unique values
395 missing
V7numeric152 unique values
76 missing
V8numeric51 unique values
1 missing
V9numeric386 unique values
76 missing
V10numeric6 unique values
873 missing
V11numeric46 unique values
76 missing
V12numeric22 unique values
1535 missing
V13numeric52 unique values
462 missing
V14nominal82 unique values
0 missing
V15nominal245 unique values
0 missing
V16nominal1407 unique values
0 missing
V17nominal1144 unique values
0 missing
V18nominal33 unique values
0 missing
V19nominal9 unique values
0 missing
V20nominal1224 unique values
0 missing
V21nominal45 unique values
0 missing
V22nominal2 unique values
0 missing
V23nominal1076 unique values
0 missing
V24nominal920 unique values
0 missing
V25nominal1350 unique values
0 missing
V26nominal826 unique values
0 missing
V27nominal16 unique values
0 missing
V28nominal862 unique values
0 missing
V29nominal1274 unique values
0 missing
V30nominal9 unique values
0 missing
V31nominal576 unique values
0 missing
V32nominal202 unique values
0 missing
V33nominal4 unique values
0 missing
V34nominal1317 unique values
0 missing
V35nominal6 unique values
0 missing
V36nominal12 unique values
0 missing
V37nominal813 unique values
0 missing
V38nominal32 unique values
0 missing
V39nominal664 unique values
0 missing
V40numeric157 unique values
74 missing
V41nominal4 unique values
0 missing
V42numeric50 unique values
1 missing
V43numeric345 unique values
384 missing
V44nominal1293 unique values
0 missing
V45nominal9 unique values
0 missing
V46nominal1165 unique values
0 missing
V47nominal7 unique values
0 missing
V48nominal33 unique values
0 missing
V49nominal244 unique values
0 missing
V50numeric149 unique values
71 missing
V51numeric38 unique values
67 missing
V52numeric5 unique values
844 missing
V53numeric23 unique values
1511 missing
V54nominal252 unique values
0 missing
V55nominal1415 unique values
0 missing
V56nominal1062 unique values
0 missing
V57nominal628 unique values
0 missing
V58nominal812 unique values
0 missing
V59numeric149 unique values
71 missing
V60nominal255 unique values
0 missing
V61nominal803 unique values
0 missing
V62nominal808 unique values
0 missing
V63nominal12 unique values
0 missing
V64numeric23 unique values
1493 missing
V65nominal21 unique values
0 missing
V66nominal1166 unique values
0 missing
V67numeric22 unique values
1523 missing
V68nominal16 unique values
0 missing
V69numeric20 unique values
1506 missing
V70nominal26 unique values
0 missing
V71nominal29 unique values
0 missing
V72numeric40 unique values
74 missing
V73nominal37 unique values
0 missing
V74nominal36 unique values
0 missing
V75numeric40 unique values
76 missing
V76nominal27 unique values
0 missing
V77nominal881 unique values
0 missing
V78nominal798 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
79
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
13059
Number of missing values in the dataset.
2000
Number of instances with at least one value missing.
26
Number of numeric attributes.
53
Number of nominal attributes.
0.04
Number of attributes divided by the number of instances.
32.91
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
67.09
Percentage of nominal attributes.
1000
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.
1000
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
1.27
Percentage of binary attributes.
100
Percentage of instances having missing values.
0.47
Average class difference between consecutive instances.
8.27
Percentage of missing values.

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