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philippine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

philippine_seed_2_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=2 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
V10numeric104 unique values
0 missing
V12numeric1949 unique values
0 missing
V13numeric1991 unique values
0 missing
V18numeric1985 unique values
0 missing
V20numeric1997 unique values
0 missing
V23numeric1999 unique values
0 missing
V24numeric1995 unique values
0 missing
V29numeric1987 unique values
0 missing
V31numeric1926 unique values
0 missing
V35numeric1986 unique values
0 missing
V42numeric1990 unique values
0 missing
V46numeric1993 unique values
0 missing
V47numeric1293 unique values
0 missing
V53numeric835 unique values
0 missing
V55numeric1985 unique values
0 missing
V59numeric1552 unique values
0 missing
V61numeric1993 unique values
0 missing
V62numeric104 unique values
0 missing
V63numeric1997 unique values
0 missing
V64numeric1954 unique values
0 missing
V66numeric1999 unique values
0 missing
V71numeric1833 unique values
0 missing
V73numeric1961 unique values
0 missing
V79numeric1965 unique values
0 missing
V83numeric1988 unique values
0 missing
V87numeric1920 unique values
0 missing
V89numeric1997 unique values
0 missing
V94numeric1997 unique values
0 missing
V97numeric1937 unique values
0 missing
V98numeric1995 unique values
0 missing
V101numeric1909 unique values
0 missing
V102numeric1991 unique values
0 missing
V114numeric1316 unique values
0 missing
V122numeric1992 unique values
0 missing
V124numeric1942 unique values
0 missing
V126numeric1999 unique values
0 missing
V127numeric1984 unique values
0 missing
V128numeric1990 unique values
0 missing
V129numeric171 unique values
0 missing
V130numeric104 unique values
0 missing
V131numeric1996 unique values
0 missing
V132numeric176 unique values
0 missing
V135numeric1947 unique values
0 missing
V138numeric1996 unique values
0 missing
V141numeric1999 unique values
0 missing
V143numeric1988 unique values
0 missing
V146numeric1997 unique values
0 missing
V148numeric1997 unique values
0 missing
V150numeric1895 unique values
0 missing
V152numeric107 unique values
0 missing
V154numeric1990 unique values
0 missing
V158numeric1991 unique values
0 missing
V160numeric1388 unique values
0 missing
V161numeric1999 unique values
0 missing
V165numeric1998 unique values
0 missing
V167numeric1998 unique values
0 missing
V169numeric1993 unique values
0 missing
V175numeric90 unique values
0 missing
V176numeric1949 unique values
0 missing
V179numeric1987 unique values
0 missing
V180numeric1982 unique values
0 missing
V184numeric1993 unique values
0 missing
V186numeric1992 unique values
0 missing
V187numeric1995 unique values
0 missing
V190numeric334 unique values
0 missing
V197numeric1979 unique values
0 missing
V199numeric406 unique values
0 missing
V207numeric1905 unique values
0 missing
V211numeric1998 unique values
0 missing
V220numeric1994 unique values
0 missing
V223numeric1994 unique values
0 missing
V224numeric1280 unique values
0 missing
V225numeric1644 unique values
0 missing
V226numeric1834 unique values
0 missing
V227numeric1949 unique values
0 missing
V232numeric1251 unique values
0 missing
V233numeric90 unique values
0 missing
V235numeric1998 unique values
0 missing
V236numeric1999 unique values
0 missing
V241numeric1912 unique values
0 missing
V244numeric1728 unique values
0 missing
V247numeric1996 unique values
0 missing
V251numeric1800 unique values
0 missing
V253numeric1997 unique values
0 missing
V258numeric1989 unique values
0 missing
V259numeric1993 unique values
0 missing
V263numeric1987 unique values
0 missing
V268numeric90 unique values
0 missing
V269numeric1996 unique values
0 missing
V270numeric1987 unique values
0 missing
V272numeric1644 unique values
0 missing
V276numeric292 unique values
0 missing
V281numeric1995 unique values
0 missing
V282numeric500 unique values
0 missing
V288numeric1946 unique values
0 missing
V291numeric1996 unique values
0 missing
V292numeric1998 unique values
0 missing
V297numeric1280 unique values
0 missing
V300numeric1996 unique values
0 missing
V302numeric1921 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
0.49
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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