Data
kick_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

kick_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Public Domain (CC0) Visibility: public Uploaded 17-11-2022 by Eddie Bergman
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Physical Sciences Transportation
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Subsampling of the dataset kick (41162) with seed=1 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, ) ```

33 features

IsBadBuy (target)nominal2 unique values
0 missing
PurchDatenumeric455 unique values
0 missing
Auctionnominal3 unique values
0 missing
VehYearnumeric10 unique values
0 missing
VehicleAgenumeric10 unique values
0 missing
Makenominal24 unique values
0 missing
Modelnominal389 unique values
0 missing
Trimnominal74 unique values
74 missing
SubModelnominal301 unique values
0 missing
Colornominal16 unique values
0 missing
Transmissionnominal2 unique values
0 missing
WheelTypeIDnominal3 unique values
87 missing
WheelTypenominal3 unique values
87 missing
VehOdonumeric1951 unique values
0 missing
Nationalitynominal4 unique values
0 missing
Sizenominal12 unique values
0 missing
TopThreeAmericanNamenominal4 unique values
0 missing
MMRAcquisitionAuctionAveragePricenumeric1673 unique values
1 missing
MMRAcquisitionAuctionCleanPricenumeric1710 unique values
1 missing
MMRAcquisitionRetailAveragePricenumeric1718 unique values
1 missing
MMRAcquisitonRetailCleanPricenumeric1726 unique values
1 missing
MMRCurrentAuctionAveragePricenumeric1648 unique values
8 missing
MMRCurrentAuctionCleanPricenumeric1682 unique values
8 missing
MMRCurrentRetailAveragePricenumeric1714 unique values
8 missing
MMRCurrentRetailCleanPricenumeric1712 unique values
8 missing
PRIMEUNITnominal2 unique values
1911 missing
AUCGUARTnominal2 unique values
1911 missing
BYRNOnominal55 unique values
0 missing
VNZIP1nominal125 unique values
0 missing
VNSTnominal35 unique values
0 missing
VehBCostnumeric890 unique values
3 missing
IsOnlineSalenominal2 unique values
0 missing
WarrantyCostnumeric180 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
4109
Number of missing values in the dataset.
1914
Number of instances with at least one value missing.
14
Number of numeric attributes.
19
Number of nominal attributes.
12.12
Percentage of binary attributes.
95.7
Percentage of instances having missing values.
0.78
Average class difference between consecutive instances.
6.23
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
42.42
Percentage of numeric attributes.
87.7
Percentage of instances belonging to the most frequent class.
57.58
Percentage of nominal attributes.
1754
Number of instances belonging to the most frequent class.
12.3
Percentage of instances belonging to the least frequent class.
246
Number of instances belonging to the least frequent class.
4
Number of binary attributes.

0 tasks

Define a new task