Subsampling of the dataset airlines (1169) 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,…
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2000 instances - 8 features - 2 classes - 0 missing values
Subsampling of the dataset airlines (1169) 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,…
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2000 instances - 8 features - 2 classes - 0 missing values
Subsampling of the dataset APSFailure (41138) 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,…
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2000 instances - 101 features - 2 classes - 19156 missing values
Subsampling of the dataset dilbert (41163) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 5 classes - 0 missing values
Subsampling of the dataset dilbert (41163) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 5 classes - 0 missing values
Subsampling of the dataset fabert (41164) 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,…
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2000 instances - 101 features - 7 classes - 0 missing values
Subsampling of the dataset jasmine (41143) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset jasmine (41143) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset steel-plates-fault (40982) with seed=3 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def…
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1941 instances - 28 features - 7 classes - 0 missing values
Subsampling of the dataset steel-plates-fault (40982) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def…
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1941 instances - 28 features - 7 classes - 0 missing values
Subsampling of the dataset APSFailure (41138) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 19825 missing values
Subsampling of the dataset dilbert (41163) 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,…
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2000 instances - 101 features - 5 classes - 0 missing values
Subsampling of the dataset dilbert (41163) 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,…
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2000 instances - 101 features - 5 classes - 0 missing values
Subsampling of the dataset dilbert (41163) 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,…
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2000 instances - 101 features - 5 classes - 0 missing values
Subsampling of the dataset steel-plates-fault (40982) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def…
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1941 instances - 28 features - 7 classes - 0 missing values
Subsampling of the dataset steel-plates-fault (40982) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def…
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1941 instances - 28 features - 7 classes - 0 missing values
Subsampling of the dataset steel-plates-fault (40982) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def…
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1941 instances - 28 features - 7 classes - 0 missing values
Subsampling of the dataset robert (41165) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset robert (41165) 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,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset robert (41165) 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,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset robert (41165) 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,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset guillermo (41159) 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,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset guillermo (41159) 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,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset riccardo (41161) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset guillermo (41159) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset guillermo (41159) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset guillermo (41159) 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,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset helena (41169) 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,…
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2000 instances - 28 features - 100 classes - 0 missing values
Subsampling of the dataset volkert (41166) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset volkert (41166) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset volkert (41166) 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,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset robert (41165) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset riccardo (41161) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset riccardo (41161) 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,…
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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset volkert (41166) 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,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset volkert (41166) 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,…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset shuttle (40685) with seed=0 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 10 features - 5 classes - 0 missing values
Subsampling of the dataset shuttle (40685) with seed=1 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self,…
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2000 instances - 10 features - 5 classes - 0 missing values
Subsampling of the dataset shuttle (40685) 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,…
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2000 instances - 10 features - 5 classes - 0 missing values
Subsampling of the dataset shuttle (40685) 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,…
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2000 instances - 10 features - 5 classes - 0 missing values
Subsampling of the dataset shuttle (40685) 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,…
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2000 instances - 10 features - 5 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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3434 instances - 420 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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13272 instances - 21 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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57580 instances - 55 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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13272 instances - 21 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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3434 instances - 420 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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38474 instances - 8 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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20634 instances - 9 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original…
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8192 instances - 22 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original…
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15000 instances - 27 features - 0 classes - 0 missing values
The dataset contains information about departure delays of airlines from years 1987 - 2013. This is a subset of the 10M version (which is once again a subset of the original dataset). Unique carrier…
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1000000 instances - 12 features - 0 classes - 0 missing values
This is a test
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57580 instances - 55 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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71090 instances - 8 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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10000 instances - 23 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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16714 instances - 11 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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20634 instances - 9 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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10000 instances - 23 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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16714 instances - 11 features - 2 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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57580 instances - 55 features - 0 classes - 0 missing values
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.…
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57580 instances - 55 features - 0 classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#child) - Number of nodes: 20 - Number of arcs: 25 - Number of parameters: 230 - Average…
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5000 instances - 20 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#mildew) - Number of nodes: 35 - Number of arcs: 46 - Number of parameters: 540150 -…
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5000 instances - 35 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#water) - Number of nodes: 32 - Number of arcs: 66 - Number of parameters: 10083 - Average…
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5000 instances - 32 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-medium.html#insurance) - Number of nodes: 27 - Number of arcs: 52 - Number of parameters: 1008 -…
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5000 instances - 27 features - classes - 0 missing values