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philippine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

philippine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
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Subsampling of the dataset philippine (41145) 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, ) ```

101 features

class (target)nominal2 unique values
0 missing
V5numeric1937 unique values
0 missing
V7numeric1982 unique values
0 missing
V8numeric1952 unique values
0 missing
V12numeric1954 unique values
0 missing
V16numeric1812 unique values
0 missing
V17numeric412 unique values
0 missing
V18numeric1988 unique values
0 missing
V20numeric1998 unique values
0 missing
V24numeric1997 unique values
0 missing
V30numeric1952 unique values
0 missing
V31numeric1943 unique values
0 missing
V32numeric1992 unique values
0 missing
V33numeric1999 unique values
0 missing
V43numeric1994 unique values
0 missing
V46numeric1991 unique values
0 missing
V51numeric1964 unique values
0 missing
V55numeric1972 unique values
0 missing
V58numeric1992 unique values
0 missing
V61numeric1989 unique values
0 missing
V63numeric1995 unique values
0 missing
V66numeric1999 unique values
0 missing
V68numeric104 unique values
0 missing
V71numeric1829 unique values
0 missing
V73numeric1950 unique values
0 missing
V74numeric1998 unique values
0 missing
V77numeric1999 unique values
0 missing
V78numeric1707 unique values
0 missing
V80numeric1993 unique values
0 missing
V87numeric1898 unique values
0 missing
V92numeric1994 unique values
0 missing
V94numeric1992 unique values
0 missing
V99numeric1999 unique values
0 missing
V101numeric1908 unique values
0 missing
V103numeric1998 unique values
0 missing
V106numeric1938 unique values
0 missing
V107numeric1952 unique values
0 missing
V108numeric1910 unique values
0 missing
V109numeric1957 unique values
0 missing
V114numeric1291 unique values
0 missing
V116numeric1837 unique values
0 missing
V121numeric1982 unique values
0 missing
V124numeric1943 unique values
0 missing
V125numeric1996 unique values
0 missing
V126numeric1999 unique values
0 missing
V127numeric1985 unique values
0 missing
V132numeric175 unique values
0 missing
V133numeric1995 unique values
0 missing
V140numeric1944 unique values
0 missing
V142numeric985 unique values
0 missing
V145numeric1994 unique values
0 missing
V151numeric1989 unique values
0 missing
V153numeric1993 unique values
0 missing
V155numeric1993 unique values
0 missing
V160numeric1378 unique values
0 missing
V168numeric89 unique values
0 missing
V171numeric1952 unique values
0 missing
V174numeric1997 unique values
0 missing
V175numeric90 unique values
0 missing
V177numeric1988 unique values
0 missing
V184numeric1994 unique values
0 missing
V186numeric1991 unique values
0 missing
V187numeric1996 unique values
0 missing
V188numeric1997 unique values
0 missing
V189numeric1992 unique values
0 missing
V191numeric1949 unique values
0 missing
V193numeric1993 unique values
0 missing
V194numeric538 unique values
0 missing
V199numeric404 unique values
0 missing
V202numeric169 unique values
0 missing
V203numeric1992 unique values
0 missing
V204numeric1996 unique values
0 missing
V205numeric1975 unique values
0 missing
V215numeric1994 unique values
0 missing
V216numeric1997 unique values
0 missing
V222numeric1989 unique values
0 missing
V225numeric1634 unique values
0 missing
V229numeric1990 unique values
0 missing
V232numeric1251 unique values
0 missing
V233numeric93 unique values
0 missing
V236numeric1998 unique values
0 missing
V238numeric1955 unique values
0 missing
V239numeric1948 unique values
0 missing
V243numeric1995 unique values
0 missing
V244numeric1741 unique values
0 missing
V247numeric1993 unique values
0 missing
V249numeric1992 unique values
0 missing
V251numeric1799 unique values
0 missing
V252numeric89 unique values
0 missing
V253numeric1997 unique values
0 missing
V255numeric1987 unique values
0 missing
V257numeric1992 unique values
0 missing
V260numeric1999 unique values
0 missing
V263numeric1996 unique values
0 missing
V265numeric1994 unique values
0 missing
V278numeric1996 unique values
0 missing
V285numeric1916 unique values
0 missing
V292numeric1997 unique values
0 missing
V296numeric1989 unique values
0 missing
V298numeric1952 unique values
0 missing
V299numeric1999 unique values
0 missing
Show first 100 features

19 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
101
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
100
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfSymbolicFeatures
0.99
Percentage of nominal attributes.
AutoCorrelation
0.52
Average class difference between consecutive instances.
PercentageOfNumericFeatures
99.01
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
0.99
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
1000
Number of instances belonging to the least frequent class.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1000
Number of instances belonging to the most frequent class.
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
Dimensionality
0.05
Number of attributes divided by the number of instances.
Show all 19 properties

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