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numerai28.6_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

numerai28.6_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 numerai28.6 (23517) 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, ) ```

22 features

attribute_21 (target)nominal2 unique values
0 missing
attribute_0numeric871 unique values
0 missing
attribute_1numeric868 unique values
0 missing
attribute_2numeric875 unique values
0 missing
attribute_3numeric873 unique values
0 missing
attribute_4numeric855 unique values
0 missing
attribute_5numeric864 unique values
0 missing
attribute_6numeric875 unique values
0 missing
attribute_7numeric873 unique values
0 missing
attribute_8numeric867 unique values
0 missing
attribute_9numeric860 unique values
0 missing
attribute_10numeric851 unique values
0 missing
attribute_11numeric867 unique values
0 missing
attribute_12numeric868 unique values
0 missing
attribute_13numeric860 unique values
0 missing
attribute_14numeric873 unique values
0 missing
attribute_15numeric865 unique values
0 missing
attribute_16numeric859 unique values
0 missing
attribute_17numeric880 unique values
0 missing
attribute_18numeric859 unique values
0 missing
attribute_19numeric865 unique values
0 missing
attribute_20numeric863 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
22
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
21
Number of numeric attributes.
1
Number of nominal attributes.
990
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
4.55
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.5
Average class difference between consecutive instances.
95.45
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
4.55
Percentage of nominal attributes.
50.5
Percentage of instances belonging to the most frequent class.
1010
Number of instances belonging to the most frequent class.
49.5
Percentage of instances belonging to the least frequent class.

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