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mfeat-factors_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

mfeat-factors_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 mfeat-factors (12) 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)nominal10 unique values
0 missing
att7numeric43 unique values
0 missing
att8numeric39 unique values
0 missing
att9numeric40 unique values
0 missing
att12numeric18 unique values
0 missing
att13numeric336 unique values
0 missing
att14numeric420 unique values
0 missing
att16numeric351 unique values
0 missing
att19numeric42 unique values
0 missing
att20numeric41 unique values
0 missing
att22numeric25 unique values
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att25numeric302 unique values
0 missing
att28numeric323 unique values
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att30numeric377 unique values
0 missing
att31numeric41 unique values
0 missing
att33numeric44 unique values
0 missing
att36numeric23 unique values
0 missing
att37numeric402 unique values
0 missing
att38numeric483 unique values
0 missing
att42numeric364 unique values
0 missing
att46numeric23 unique values
0 missing
att51numeric471 unique values
0 missing
att53numeric375 unique values
0 missing
att55numeric42 unique values
0 missing
att56numeric40 unique values
0 missing
att59numeric23 unique values
0 missing
att61numeric302 unique values
0 missing
att62numeric348 unique values
0 missing
att71numeric20 unique values
0 missing
att75numeric435 unique values
0 missing
att76numeric395 unique values
0 missing
att77numeric313 unique values
0 missing
att78numeric432 unique values
0 missing
att81numeric41 unique values
0 missing
att82numeric25 unique values
0 missing
att84numeric20 unique values
0 missing
att85numeric301 unique values
0 missing
att86numeric460 unique values
0 missing
att88numeric448 unique values
0 missing
att90numeric405 unique values
0 missing
att91numeric42 unique values
0 missing
att92numeric36 unique values
0 missing
att93numeric45 unique values
0 missing
att94numeric25 unique values
0 missing
att95numeric18 unique values
0 missing
att96numeric23 unique values
0 missing
att97numeric473 unique values
0 missing
att98numeric390 unique values
0 missing
att99numeric352 unique values
0 missing
att100numeric494 unique values
0 missing
att102numeric394 unique values
0 missing
att104numeric44 unique values
0 missing
att106numeric23 unique values
0 missing
att108numeric20 unique values
0 missing
att110numeric342 unique values
0 missing
att112numeric297 unique values
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att113numeric407 unique values
0 missing
att114numeric401 unique values
0 missing
att116numeric30 unique values
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att120numeric24 unique values
0 missing
att122numeric341 unique values
0 missing
att123numeric548 unique values
0 missing
att124numeric398 unique values
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att128numeric32 unique values
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att129numeric45 unique values
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att132numeric26 unique values
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att137numeric335 unique values
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att138numeric326 unique values
0 missing
att139numeric37 unique values
0 missing
att140numeric35 unique values
0 missing
att143numeric19 unique values
0 missing
att145numeric278 unique values
0 missing
att146numeric434 unique values
0 missing
att148numeric492 unique values
0 missing
att150numeric355 unique values
0 missing
att152numeric32 unique values
0 missing
att156numeric25 unique values
0 missing
att157numeric379 unique values
0 missing
att160numeric403 unique values
0 missing
att161numeric327 unique values
0 missing
att162numeric449 unique values
0 missing
att163numeric44 unique values
0 missing
att166numeric25 unique values
0 missing
att167numeric22 unique values
0 missing
att168numeric19 unique values
0 missing
att170numeric394 unique values
0 missing
att171numeric435 unique values
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att176numeric39 unique values
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att177numeric45 unique values
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att178numeric18 unique values
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att179numeric22 unique values
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att186numeric434 unique values
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att191numeric25 unique values
0 missing
att193numeric445 unique values
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att195numeric474 unique values
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att202numeric25 unique values
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att206numeric330 unique values
0 missing
att207numeric550 unique values
0 missing
att210numeric391 unique values
0 missing
att213numeric36 unique values
0 missing
att214numeric15 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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