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philippine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

philippine_seed_3_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=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, ) ```

101 features

class (target)nominal2 unique values
0 missing
V1numeric1992 unique values
0 missing
V2numeric555 unique values
0 missing
V8numeric1952 unique values
0 missing
V9numeric1909 unique values
0 missing
V12numeric1960 unique values
0 missing
V18numeric1994 unique values
0 missing
V20numeric1998 unique values
0 missing
V21numeric1983 unique values
0 missing
V25numeric1912 unique values
0 missing
V26numeric150 unique values
0 missing
V32numeric1998 unique values
0 missing
V36numeric1905 unique values
0 missing
V38numeric1998 unique values
0 missing
V39numeric1455 unique values
0 missing
V41numeric1713 unique values
0 missing
V51numeric1944 unique values
0 missing
V58numeric1992 unique values
0 missing
V60numeric1993 unique values
0 missing
V63numeric1994 unique values
0 missing
V64numeric1964 unique values
0 missing
V67numeric1992 unique values
0 missing
V68numeric106 unique values
0 missing
V69numeric1832 unique values
0 missing
V70numeric1994 unique values
0 missing
V73numeric1956 unique values
0 missing
V76numeric1995 unique values
0 missing
V78numeric1708 unique values
0 missing
V81numeric1989 unique values
0 missing
V84numeric1997 unique values
0 missing
V85numeric1982 unique values
0 missing
V90numeric447 unique values
0 missing
V96numeric1998 unique values
0 missing
V98numeric1993 unique values
0 missing
V99numeric1996 unique values
0 missing
V101numeric1896 unique values
0 missing
V102numeric1995 unique values
0 missing
V104numeric102 unique values
0 missing
V107numeric1953 unique values
0 missing
V113numeric1933 unique values
0 missing
V117numeric537 unique values
0 missing
V120numeric1989 unique values
0 missing
V122numeric1992 unique values
0 missing
V124numeric1950 unique values
0 missing
V126numeric2000 unique values
0 missing
V128numeric1996 unique values
0 missing
V138numeric1995 unique values
0 missing
V139numeric1979 unique values
0 missing
V140numeric1918 unique values
0 missing
V144numeric1985 unique values
0 missing
V153numeric1996 unique values
0 missing
V156numeric451 unique values
0 missing
V157numeric1999 unique values
0 missing
V159numeric418 unique values
0 missing
V160numeric1390 unique values
0 missing
V167numeric1997 unique values
0 missing
V170numeric1985 unique values
0 missing
V172numeric1994 unique values
0 missing
V176numeric1953 unique values
0 missing
V177numeric1991 unique values
0 missing
V181numeric1800 unique values
0 missing
V187numeric1995 unique values
0 missing
V188numeric1998 unique values
0 missing
V190numeric336 unique values
0 missing
V193numeric1995 unique values
0 missing
V201numeric1990 unique values
0 missing
V202numeric169 unique values
0 missing
V205numeric1985 unique values
0 missing
V206numeric1998 unique values
0 missing
V207numeric1907 unique values
0 missing
V211numeric1997 unique values
0 missing
V215numeric1994 unique values
0 missing
V228numeric1999 unique values
0 missing
V230numeric1676 unique values
0 missing
V231numeric27 unique values
0 missing
V234numeric1984 unique values
0 missing
V239numeric1949 unique values
0 missing
V241numeric1906 unique values
0 missing
V243numeric1996 unique values
0 missing
V246numeric1981 unique values
0 missing
V248numeric1992 unique values
0 missing
V252numeric88 unique values
0 missing
V255numeric1988 unique values
0 missing
V258numeric1977 unique values
0 missing
V259numeric1998 unique values
0 missing
V261numeric2000 unique values
0 missing
V263numeric1986 unique values
0 missing
V265numeric1995 unique values
0 missing
V266numeric1997 unique values
0 missing
V268numeric91 unique values
0 missing
V271numeric1994 unique values
0 missing
V272numeric1626 unique values
0 missing
V273numeric1998 unique values
0 missing
V274numeric1982 unique values
0 missing
V277numeric1992 unique values
0 missing
V279numeric1992 unique values
0 missing
V283numeric1995 unique values
0 missing
V287numeric1987 unique values
0 missing
V291numeric1997 unique values
0 missing
V302numeric1926 unique values
0 missing
V305numeric1957 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
0.5
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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