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steel-plates-fault_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

steel-plates-fault_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 steel-plates-fault (40982) 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, ) ```

28 features

target (target)nominal7 unique values
0 missing
V1numeric962 unique values
0 missing
V2numeric994 unique values
0 missing
V3numeric1939 unique values
0 missing
V4numeric1940 unique values
0 missing
V5numeric920 unique values
0 missing
V6numeric399 unique values
0 missing
V7numeric317 unique values
0 missing
V8numeric1909 unique values
0 missing
V9numeric161 unique values
0 missing
V10numeric100 unique values
0 missing
V11numeric84 unique values
0 missing
V12numeric2 unique values
0 missing
V13numeric2 unique values
0 missing
V14numeric24 unique values
0 missing
V15numeric1387 unique values
0 missing
V16numeric1338 unique values
0 missing
V17numeric770 unique values
0 missing
V18numeric454 unique values
0 missing
V19numeric818 unique values
0 missing
V20numeric648 unique values
0 missing
V21numeric3 unique values
0 missing
V22numeric914 unique values
0 missing
V23numeric183 unique values
0 missing
V24numeric217 unique values
0 missing
V25numeric918 unique values
0 missing
V26numeric1522 unique values
0 missing
V27numeric388 unique values
0 missing

19 properties

1941
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
7
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.
27
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
1
Average class difference between consecutive instances.
96.43
Percentage of numeric attributes.
0.01
Number of attributes divided by the number of instances.
3.57
Percentage of nominal attributes.
34.67
Percentage of instances belonging to the most frequent class.
673
Number of instances belonging to the most frequent class.
2.83
Percentage of instances belonging to the least frequent class.
55
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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