Data
Higgs_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

Higgs_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by Eddie Bergman
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Subsampling of the dataset Higgs (44129) 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, ) ```

25 features

target (target)nominal2 unique values
0 missing
lepton_pTnumeric1749 unique values
0 missing
lepton_etanumeric1588 unique values
0 missing
lepton_phinumeric1716 unique values
0 missing
missing_energy_magnitudenumeric1998 unique values
0 missing
missing_energy_phinumeric1998 unique values
0 missing
jet_1_ptnumeric1863 unique values
0 missing
jet_1_etanumeric1537 unique values
0 missing
jet_1_phinumeric1700 unique values
0 missing
jet_2_ptnumeric1813 unique values
0 missing
jet_2_etanumeric1547 unique values
0 missing
jet_2_phinumeric1704 unique values
0 missing
jet_3_ptnumeric1781 unique values
0 missing
jet_3_etanumeric1596 unique values
0 missing
jet_3_phinumeric1711 unique values
0 missing
jet_4_ptnumeric1673 unique values
0 missing
jet_4_etanumeric1603 unique values
0 missing
jet_4_phinumeric1701 unique values
0 missing
m_jjnumeric1997 unique values
0 missing
m_jjjnumeric1981 unique values
0 missing
m_lvnumeric1925 unique values
0 missing
m_jlvnumeric1992 unique values
0 missing
m_bbnumeric1993 unique values
0 missing
m_wbbnumeric1991 unique values
0 missing
m_wwbbnumeric1997 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
25
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.
24
Number of numeric attributes.
1
Number of nominal attributes.
4
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.
96
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
4
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.

0 tasks

Define a new task