Data
MagicTelescope_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

MagicTelescope_seed_0_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 MagicTelescope (44125) with seed=0 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, ) ```

11 features

class (target)nominal2 unique values
0 missing
fLength:numeric1993 unique values
0 missing
fWidth:numeric1979 unique values
0 missing
fSize:numeric1656 unique values
0 missing
fConc:numeric1705 unique values
0 missing
fConc1:numeric1558 unique values
0 missing
fAsym:numeric1992 unique values
0 missing
fM3Long:numeric1994 unique values
0 missing
fM3Trans:numeric1982 unique values
0 missing
fAlpha:numeric1986 unique values
0 missing
fDist:numeric1993 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
11
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.
10
Number of numeric attributes.
1
Number of nominal attributes.
9.09
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.51
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
90.91
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
9.09
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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