Data
sf-police-incidents_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

sf-police-incidents_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Public Domain (CC0) Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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Subsampling of the dataset sf-police-incidents (42732) 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, ) ```

9 features

ViolentCrime (target)nominal2 unique values
0 missing
Hournumeric24 unique values
0 missing
DayOfWeeknominal7 unique values
0 missing
Monthnominal12 unique values
0 missing
Yearnominal16 unique values
0 missing
PdDistrictnominal10 unique values
0 missing
Addressnominal1527 unique values
0 missing
Xnumeric1593 unique values
0 missing
Ynumeric1593 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
9
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.
6
Number of nominal attributes.
11.11
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.79
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
33.33
Percentage of numeric attributes.
87.85
Percentage of instances belonging to the most frequent class.
66.67
Percentage of nominal attributes.
1757
Number of instances belonging to the most frequent class.
12.15
Percentage of instances belonging to the least frequent class.
243
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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