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philippine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

philippine_seed_4_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=4 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
V8numeric1961 unique values
0 missing
V11numeric1996 unique values
0 missing
V14numeric1993 unique values
0 missing
V18numeric1988 unique values
0 missing
V22numeric1993 unique values
0 missing
V24numeric1993 unique values
0 missing
V33numeric1997 unique values
0 missing
V35numeric1994 unique values
0 missing
V39numeric1464 unique values
0 missing
V40numeric2000 unique values
0 missing
V44numeric1670 unique values
0 missing
V45numeric1998 unique values
0 missing
V50numeric1995 unique values
0 missing
V52numeric1937 unique values
0 missing
V53numeric853 unique values
0 missing
V54numeric1986 unique values
0 missing
V57numeric979 unique values
0 missing
V60numeric1995 unique values
0 missing
V63numeric1997 unique values
0 missing
V67numeric1991 unique values
0 missing
V75numeric1999 unique values
0 missing
V76numeric1995 unique values
0 missing
V78numeric1710 unique values
0 missing
V83numeric1985 unique values
0 missing
V89numeric1998 unique values
0 missing
V94numeric1995 unique values
0 missing
V96numeric1999 unique values
0 missing
V99numeric1996 unique values
0 missing
V101numeric1918 unique values
0 missing
V103numeric2000 unique values
0 missing
V106numeric1959 unique values
0 missing
V108numeric1896 unique values
0 missing
V109numeric1945 unique values
0 missing
V111numeric1909 unique values
0 missing
V115numeric1951 unique values
0 missing
V118numeric1993 unique values
0 missing
V125numeric1996 unique values
0 missing
V129numeric172 unique values
0 missing
V130numeric105 unique values
0 missing
V132numeric180 unique values
0 missing
V133numeric1993 unique values
0 missing
V134numeric1944 unique values
0 missing
V137numeric1994 unique values
0 missing
V139numeric1986 unique values
0 missing
V140numeric1936 unique values
0 missing
V141numeric1997 unique values
0 missing
V143numeric1992 unique values
0 missing
V145numeric1994 unique values
0 missing
V147numeric88 unique values
0 missing
V151numeric1992 unique values
0 missing
V152numeric103 unique values
0 missing
V153numeric1997 unique values
0 missing
V157numeric1997 unique values
0 missing
V158numeric1997 unique values
0 missing
V161numeric1999 unique values
0 missing
V170numeric1992 unique values
0 missing
V175numeric90 unique values
0 missing
V177numeric1986 unique values
0 missing
V179numeric1988 unique values
0 missing
V186numeric1988 unique values
0 missing
V187numeric1993 unique values
0 missing
V192numeric1986 unique values
0 missing
V196numeric1941 unique values
0 missing
V197numeric1989 unique values
0 missing
V199numeric404 unique values
0 missing
V201numeric1996 unique values
0 missing
V208numeric1983 unique values
0 missing
V209numeric1980 unique values
0 missing
V210numeric1519 unique values
0 missing
V215numeric1991 unique values
0 missing
V223numeric1992 unique values
0 missing
V224numeric1267 unique values
0 missing
V226numeric1841 unique values
0 missing
V227numeric1948 unique values
0 missing
V229numeric1993 unique values
0 missing
V235numeric1998 unique values
0 missing
V238numeric1958 unique values
0 missing
V242numeric1992 unique values
0 missing
V244numeric1740 unique values
0 missing
V246numeric1978 unique values
0 missing
V250numeric1913 unique values
0 missing
V254numeric1992 unique values
0 missing
V258numeric1992 unique values
0 missing
V259numeric1997 unique values
0 missing
V260numeric1997 unique values
0 missing
V261numeric1998 unique values
0 missing
V265numeric1996 unique values
0 missing
V271numeric1999 unique values
0 missing
V279numeric1997 unique values
0 missing
V282numeric499 unique values
0 missing
V283numeric1991 unique values
0 missing
V285numeric1912 unique values
0 missing
V288numeric1958 unique values
0 missing
V289numeric1987 unique values
0 missing
V290numeric1994 unique values
0 missing
V291numeric1994 unique values
0 missing
V292numeric1995 unique values
0 missing
V293numeric180 unique values
0 missing
V296numeric1988 unique values
0 missing
V300numeric1994 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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