Data
yeast_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

yeast_seed_1_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 yeast (181) 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, ) ```

9 features

class_protein_localization (target)nominal10 unique values
0 missing
mcgnumeric81 unique values
0 missing
gvhnumeric79 unique values
0 missing
almnumeric53 unique values
0 missing
mitnumeric78 unique values
0 missing
erlnumeric2 unique values
0 missing
poxnumeric3 unique values
0 missing
vacnumeric48 unique values
0 missing
nucnumeric68 unique values
0 missing

19 properties

1484
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
10
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.
8
Number of numeric attributes.
1
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
88.89
Percentage of numeric attributes.
31.2
Percentage of instances belonging to the most frequent class.
11.11
Percentage of nominal attributes.
463
Number of instances belonging to the most frequent class.
0.34
Percentage of instances belonging to the least frequent class.
5
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.52
Average class difference between consecutive instances.
0
Percentage of missing values.

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