Data
kdd_ipums_la_97-small_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

kdd_ipums_la_97-small_seed_2_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 kdd_ipums_la_97-small (44124) with seed=2 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, ) ```

21 features

binaryClass (target)nominal2 unique values
0 missing
valuenumeric11 unique values
0 missing
rentnumeric97 unique values
0 missing
ftotincnumeric322 unique values
0 missing
momlocnumeric8 unique values
0 missing
famsizenumeric13 unique values
0 missing
nchildnumeric10 unique values
0 missing
eldchnumeric54 unique values
0 missing
yngchnumeric53 unique values
0 missing
nsibsnumeric10 unique values
0 missing
agenumeric94 unique values
0 missing
occscorenumeric43 unique values
0 missing
seinumeric74 unique values
0 missing
inctotnumeric226 unique values
0 missing
incwagenumeric186 unique values
0 missing
incbusnumeric66 unique values
0 missing
incfarmnumeric8 unique values
0 missing
incssnumeric29 unique values
0 missing
incwelfrnumeric28 unique values
0 missing
incothernumeric63 unique values
0 missing
povertynumeric435 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
21
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.
20
Number of numeric attributes.
1
Number of nominal attributes.
4.76
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.5
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
95.24
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
4.76
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.

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