DEVELOPMENT... OpenML
Data
KDDCup09-Upselling_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

KDDCup09-Upselling_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Subsampling of the dataset KDDCup09-Upselling (43072) 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

upselling (target)nominal2 unique values
0 missing
Var578numeric2 unique values
0 missing
Var593numeric1 unique values
0 missing
Var820numeric1 unique values
0 missing
Var850numeric2 unique values
0 missing
Var1143numeric1 unique values
0 missing
Var1366numeric2 unique values
0 missing
Var1445numeric2 unique values
0 missing
Var1505numeric1 unique values
0 missing
Var1558numeric8 unique values
0 missing
Var1597numeric1 unique values
0 missing
Var1623numeric1424 unique values
0 missing
Var2230numeric41 unique values
0 missing
Var2785numeric32 unique values
0 missing
Var2789numeric72 unique values
0 missing
Var2990numeric21 unique values
0 missing
Var3006numeric1961 unique values
0 missing
Var3177numeric1 unique values
0 missing
Var3227numeric2 unique values
0 missing
Var3265numeric4 unique values
0 missing
Var3330numeric7 unique values
0 missing
Var3862numeric1 unique values
0 missing
Var3864numeric2 unique values
0 missing
Var3880numeric2 unique values
0 missing
Var4082numeric2 unique values
0 missing
Var4428numeric365 unique values
0 missing
Var4540numeric2 unique values
0 missing
Var4733numeric2 unique values
0 missing
Var4951numeric1 unique values
0 missing
Var4969numeric64 unique values
0 missing
Var5058numeric74 unique values
0 missing
Var5145numeric1 unique values
0 missing
Var5652numeric1 unique values
0 missing
Var5822numeric6 unique values
0 missing
Var6057numeric1 unique values
0 missing
Var6137numeric1 unique values
0 missing
Var6282numeric180 unique values
0 missing
Var6426numeric1 unique values
0 missing
Var6532numeric2 unique values
0 missing
Var6565numeric650 unique values
0 missing
Var6627numeric2 unique values
1978 missing
Var6686numeric4 unique values
0 missing
Var6693numeric2 unique values
0 missing
Var6761numeric13 unique values
0 missing
Var7007numeric2 unique values
0 missing
Var7030numeric23 unique values
1958 missing
Var7099numeric2 unique values
0 missing
Var7249numeric7 unique values
0 missing
Var7430numeric1 unique values
0 missing
Var7438numeric1 unique values
0 missing
Var7481numeric4 unique values
0 missing
Var7601numeric17 unique values
0 missing
Var7698numeric8 unique values
0 missing
Var7778numeric1 unique values
0 missing
Var7802numeric32 unique values
0 missing
Var8287numeric2 unique values
0 missing
Var8349numeric2 unique values
0 missing
Var8474numeric1 unique values
0 missing
Var8620numeric1 unique values
0 missing
Var8843numeric1 unique values
0 missing
Var8844numeric1 unique values
0 missing
Var8847numeric2 unique values
0 missing
Var8903numeric1 unique values
1978 missing
Var9151numeric1 unique values
0 missing
Var9407numeric4 unique values
0 missing
Var9441numeric11 unique values
0 missing
Var9598numeric1 unique values
0 missing
Var9759numeric1 unique values
0 missing
Var9941numeric2 unique values
0 missing
Var10080numeric2 unique values
0 missing
Var10122numeric64 unique values
0 missing
Var10150numeric1 unique values
0 missing
Var10296numeric1 unique values
0 missing
Var10323numeric7 unique values
0 missing
Var10328numeric2 unique values
0 missing
Var10421numeric6 unique values
0 missing
Var10808numeric1955 unique values
0 missing
Var11123numeric2 unique values
0 missing
Var11473numeric2 unique values
0 missing
Var11535numeric2 unique values
0 missing
Var11695numeric55 unique values
0 missing
Var12066numeric1 unique values
0 missing
Var12078numeric6 unique values
0 missing
Var12369numeric5 unique values
0 missing
Var12420numeric1482 unique values
0 missing
Var12649numeric1 unique values
0 missing
Var12850numeric5 unique values
0 missing
Var12899numeric1 unique values
0 missing
Var13045numeric2 unique values
0 missing
Var13064numeric1 unique values
0 missing
Var13170numeric1 unique values
0 missing
Var13256numeric23 unique values
0 missing
Var13305numeric2 unique values
0 missing
Var13503numeric1 unique values
0 missing
Var13753numeric1 unique values
0 missing
Var13895numeric1 unique values
0 missing
Var14044numeric420 unique values
0 missing
Var14263numeric2 unique values
0 missing
Var14381numeric1 unique values
0 missing
Var14531numeric1 unique values
0 missing
Var14737numeric9 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
5914
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
2000
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
0.86
Average class difference between consecutive instances.
PercentageOfNumericFeatures
99.01
Percentage of numeric attributes.
PercentageOfMissingValues
2.93
Percentage of missing values.
PercentageOfInstancesWithMissingValues
100
Percentage of instances having missing values.
PercentageOfBinaryFeatures
0.99
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
147
Number of instances belonging to the least frequent class.
MinorityClassPercentage
7.35
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1853
Number of instances belonging to the most frequent class.
MajorityClassPercentage
92.65
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

0 tasks

Define a new task