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compass_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

compass_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF See source Visibility: public Uploaded 17-11-2022 by David Wilson
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Subsampling of the dataset compass (44162) 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, ) ```

18 features

is_recid (target)nominal2 unique values
0 missing
c_days_from_compasnumeric196 unique values
0 missing
endnumeric929 unique values
0 missing
v_score_textnominal3 unique values
0 missing
v_decile_scorenominal10 unique values
0 missing
v_type_of_assessmentnominal1 unique values
0 missing
score_textnominal3 unique values
0 missing
decile_score.1nominal10 unique values
0 missing
c_charge_degreenominal10 unique values
0 missing
sexnominal2 unique values
0 missing
days_b_screening_arrestnumeric220 unique values
0 missing
priors_countnumeric31 unique values
0 missing
juv_other_countnumeric6 unique values
0 missing
juv_misd_countnumeric8 unique values
0 missing
juv_fel_countnumeric7 unique values
0 missing
racenominal6 unique values
0 missing
age_catnominal3 unique values
0 missing
agenumeric56 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
18
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.
8
Number of numeric attributes.
10
Number of nominal attributes.
55.56
Percentage of nominal attributes.
0.51
Average class difference between consecutive instances.
44.44
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
11.11
Percentage of binary attributes.
2
Number of binary attributes.
1000
Number of instances belonging to the least frequent class.
50
Percentage of instances belonging to the least frequent class.
1000
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the most frequent class.
0.01
Number of attributes divided by the number of instances.

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