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mfeat-factors_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

mfeat-factors_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 mfeat-factors (12) 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)nominal10 unique values
0 missing
att6numeric419 unique values
0 missing
att8numeric39 unique values
0 missing
att9numeric40 unique values
0 missing
att11numeric19 unique values
0 missing
att15numeric421 unique values
0 missing
att20numeric41 unique values
0 missing
att22numeric25 unique values
0 missing
att23numeric18 unique values
0 missing
att24numeric21 unique values
0 missing
att28numeric323 unique values
0 missing
att30numeric377 unique values
0 missing
att34numeric20 unique values
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att36numeric23 unique values
0 missing
att39numeric468 unique values
0 missing
att40numeric411 unique values
0 missing
att43numeric34 unique values
0 missing
att47numeric22 unique values
0 missing
att48numeric23 unique values
0 missing
att49numeric300 unique values
0 missing
att50numeric484 unique values
0 missing
att55numeric42 unique values
0 missing
att57numeric44 unique values
0 missing
att61numeric302 unique values
0 missing
att62numeric348 unique values
0 missing
att63numeric464 unique values
0 missing
att65numeric449 unique values
0 missing
att67numeric41 unique values
0 missing
att72numeric25 unique values
0 missing
att74numeric361 unique values
0 missing
att75numeric435 unique values
0 missing
att77numeric313 unique values
0 missing
att81numeric41 unique values
0 missing
att83numeric20 unique values
0 missing
att84numeric20 unique values
0 missing
att85numeric301 unique values
0 missing
att86numeric460 unique values
0 missing
att87numeric427 unique values
0 missing
att90numeric405 unique values
0 missing
att91numeric42 unique values
0 missing
att93numeric45 unique values
0 missing
att94numeric25 unique values
0 missing
att95numeric18 unique values
0 missing
att96numeric23 unique values
0 missing
att98numeric390 unique values
0 missing
att99numeric352 unique values
0 missing
att104numeric44 unique values
0 missing
att105numeric46 unique values
0 missing
att108numeric20 unique values
0 missing
att109numeric450 unique values
0 missing
att110numeric342 unique values
0 missing
att112numeric297 unique values
0 missing
att114numeric401 unique values
0 missing
att116numeric30 unique values
0 missing
att117numeric41 unique values
0 missing
att120numeric24 unique values
0 missing
att123numeric548 unique values
0 missing
att125numeric411 unique values
0 missing
att127numeric43 unique values
0 missing
att130numeric23 unique values
0 missing
att131numeric19 unique values
0 missing
att135numeric590 unique values
0 missing
att137numeric335 unique values
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att138numeric326 unique values
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att140numeric35 unique values
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att142numeric23 unique values
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att144numeric23 unique values
0 missing
att145numeric278 unique values
0 missing
att146numeric434 unique values
0 missing
att150numeric355 unique values
0 missing
att151numeric37 unique values
0 missing
att152numeric32 unique values
0 missing
att153numeric35 unique values
0 missing
att155numeric18 unique values
0 missing
att158numeric398 unique values
0 missing
att160numeric403 unique values
0 missing
att163numeric44 unique values
0 missing
att165numeric46 unique values
0 missing
att168numeric19 unique values
0 missing
att170numeric394 unique values
0 missing
att171numeric435 unique values
0 missing
att172numeric314 unique values
0 missing
att175numeric44 unique values
0 missing
att177numeric45 unique values
0 missing
att184numeric416 unique values
0 missing
att185numeric460 unique values
0 missing
att187numeric43 unique values
0 missing
att188numeric43 unique values
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att190numeric18 unique values
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att192numeric25 unique values
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att194numeric517 unique values
0 missing
att195numeric474 unique values
0 missing
att199numeric46 unique values
0 missing
att202numeric25 unique values
0 missing
att204numeric19 unique values
0 missing
att205numeric303 unique values
0 missing
att208numeric472 unique values
0 missing
att209numeric292 unique values
0 missing
att210numeric391 unique values
0 missing
att213numeric36 unique values
0 missing
att216numeric24 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
10
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
1
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
Percentage of binary attributes.
NumberOfBinaryFeatures
0
Number of binary attributes.
MinorityClassSize
200
Number of instances belonging to the least frequent class.
MinorityClassPercentage
10
Percentage of instances belonging to the least frequent class.
MajorityClassSize
200
Number of instances belonging to the most frequent class.
MajorityClassPercentage
10
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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