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connect-4_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

connect-4_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF public Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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Subsampling of the dataset connect-4 (40668) 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, ) ```

43 features

class (target)nominal3 unique values
0 missing
a1nominal3 unique values
0 missing
a2nominal3 unique values
0 missing
a3nominal3 unique values
0 missing
a4nominal3 unique values
0 missing
a5nominal3 unique values
0 missing
a6nominal3 unique values
0 missing
b1nominal3 unique values
0 missing
b2nominal3 unique values
0 missing
b3nominal3 unique values
0 missing
b4nominal3 unique values
0 missing
b5nominal3 unique values
0 missing
b6nominal3 unique values
0 missing
c1nominal3 unique values
0 missing
c2nominal3 unique values
0 missing
c3nominal3 unique values
0 missing
c4nominal3 unique values
0 missing
c5nominal3 unique values
0 missing
c6nominal3 unique values
0 missing
d1nominal3 unique values
0 missing
d2nominal3 unique values
0 missing
d3nominal3 unique values
0 missing
d4nominal3 unique values
0 missing
d5nominal3 unique values
0 missing
d6nominal3 unique values
0 missing
e1nominal3 unique values
0 missing
e2nominal3 unique values
0 missing
e3nominal3 unique values
0 missing
e4nominal3 unique values
0 missing
e5nominal3 unique values
0 missing
e6nominal3 unique values
0 missing
f1nominal3 unique values
0 missing
f2nominal3 unique values
0 missing
f3nominal3 unique values
0 missing
f4nominal3 unique values
0 missing
f5nominal3 unique values
0 missing
f6nominal2 unique values
0 missing
g1nominal3 unique values
0 missing
g2nominal3 unique values
0 missing
g3nominal3 unique values
0 missing
g4nominal3 unique values
0 missing
g5nominal3 unique values
0 missing
g6nominal2 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
43
Number of attributes (columns) of the dataset.
3
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.
0
Number of numeric attributes.
43
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.5
Average class difference between consecutive instances.
0
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
65.85
Percentage of instances belonging to the most frequent class.
100
Percentage of nominal attributes.
1317
Number of instances belonging to the most frequent class.
9.55
Percentage of instances belonging to the least frequent class.
191
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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