Data
airlines_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

airlines_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 airlines (1169) 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, ) ```

8 features

Delay (target)nominal2 unique values
0 missing
Airlinenominal18 unique values
0 missing
Flightnumeric1603 unique values
0 missing
AirportFromnominal182 unique values
0 missing
AirportTonominal199 unique values
0 missing
DayOfWeeknominal7 unique values
0 missing
Timenumeric499 unique values
0 missing
Lengthnumeric289 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
8
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.
3
Number of numeric attributes.
5
Number of nominal attributes.
12.5
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
Number of attributes divided by the number of instances.
37.5
Percentage of numeric attributes.
55.45
Percentage of instances belonging to the most frequent class.
62.5
Percentage of nominal attributes.
1109
Number of instances belonging to the most frequent class.
44.55
Percentage of instances belonging to the least frequent class.
891
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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