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house_16H_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

house_16H_seed_3_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 house_16H (44123) 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, ) ```

17 features

binaryClass (target)nominal2 unique values
0 missing
P1numeric1710 unique values
0 missing
P5p1numeric1880 unique values
0 missing
P6p2numeric1444 unique values
0 missing
P11p4numeric1952 unique values
0 missing
P14p9numeric1841 unique values
0 missing
P15p1numeric1929 unique values
0 missing
P15p3numeric940 unique values
0 missing
P16p2numeric1857 unique values
0 missing
P18p2numeric1230 unique values
0 missing
P27p4numeric1637 unique values
0 missing
H2p2numeric1860 unique values
0 missing
H8p2numeric1281 unique values
0 missing
H10p1numeric1435 unique values
0 missing
H13p1numeric1915 unique values
0 missing
H18pAnumeric1344 unique values
0 missing
H40p4numeric527 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
17
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.
16
Number of numeric attributes.
1
Number of nominal attributes.
1000
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
5.88
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.
94.12
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
5.88
Percentage of nominal attributes.
1000
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.

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