Subsampling of the dataset sylvine (41146) 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 - 21 features - 2 classes - 0 missing values
Subsampling of the dataset Satellite (40900) 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 - 37 features - 2 classes - 0 missing values
Subsampling of the dataset Fashion-MNIST (40996) 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(…
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2000 instances - 101 features - 10 classes - 0 missing values
Subsampling of the dataset Satellite (40900) 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 - 37 features - 2 classes - 0 missing values
Subsampling of the dataset Satellite (40900) 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 - 37 features - 2 classes - 0 missing values
Subsampling of the dataset Satellite (40900) 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 - 37 features - 2 classes - 0 missing values
Subsampling of the dataset wine-quality-white (40498) 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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2000 instances - 12 features - 7 classes - 0 missing values
Subsampling of the dataset wine-quality-white (40498) 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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2000 instances - 12 features - 7 classes - 0 missing values
Subsampling of the dataset Satellite (40900) 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 - 37 features - 2 classes - 0 missing values
Subsampling of the dataset wine-quality-white (40498) 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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2000 instances - 12 features - 7 classes - 0 missing values
Subsampling of the dataset wine-quality-white (40498) 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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2000 instances - 12 features - 7 classes - 0 missing values
Subsampling of the dataset wine-quality-white (40498) 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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2000 instances - 12 features - 7 classes - 0 missing values
Subsampling of the dataset okcupid-stem (42734) 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(…
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2000 instances - 20 features - 3 classes - 5992 missing values
Subsampling of the dataset okcupid-stem (42734) 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(…
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2000 instances - 20 features - 3 classes - 6050 missing values
Subsampling of the dataset sf-police-incidents (42732) 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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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset sf-police-incidents (42732) 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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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset KDDCup99 (42746) 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 - 42 features - 8 classes - 0 missing values
Subsampling of the dataset KDDCup99 (42746) 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 - 42 features - 8 classes - 0 missing values
Subsampling of the dataset KDDCup99 (42746) 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 - 42 features - 8 classes - 0 missing values
Subsampling of the dataset porto-seguro (42742) 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(…
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2000 instances - 58 features - 2 classes - 2775 missing values
Subsampling of the dataset porto-seguro (42742) 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(…
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2000 instances - 58 features - 2 classes - 2855 missing values
Subsampling of the dataset porto-seguro (42742) 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(…
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2000 instances - 58 features - 2 classes - 2863 missing values
Subsampling of the dataset porto-seguro (42742) 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(…
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2000 instances - 58 features - 2 classes - 2837 missing values
Subsampling of the dataset KDDCup99 (42746) 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 - 42 features - 8 classes - 0 missing values
Subsampling of the dataset KDDCup99 (42746) 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 - 42 features - 8 classes - 0 missing values
Subsampling of the dataset porto-seguro (42742) 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(…
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2000 instances - 58 features - 2 classes - 2846 missing values
Subsampling of the dataset KDDCup09-Upselling (43072) 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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2000 instances - 101 features - 2 classes - 3570 missing values
Subsampling of the dataset KDDCup09-Upselling (43072) 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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2000 instances - 101 features - 2 classes - 7121 missing values
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…
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2000 instances - 101 features - 2 classes - 5914 missing values
Subsampling of the dataset KDDCup09-Upselling (43072) 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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2000 instances - 101 features - 2 classes - 0 missing values
Subsampling of the dataset KDDCup09-Upselling (43072) 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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2000 instances - 101 features - 2 classes - 9426 missing values
The original dataset for 'ECG5000' is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record 'chf07'. It was originally published in…
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4998 instances - 141 features - 0 classes - 0 missing values
The original dataset for 'ECG5000' is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record 'chf07'. It was originally published in…
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4998 instances - 141 features - 0 classes - 0 missing values
Subsampling of the dataset sf-police-incidents (42732) 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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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset sf-police-incidents (42732) 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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2000 instances - 9 features - 2 classes - 0 missing values
Subsampling of the dataset sf-police-incidents (42732) 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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2000 instances - 9 features - 2 classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#diabetes) - Number of nodes: 413 - Number of arcs: 602 - Number of parameters: 429409…
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5000 instances - 413 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-large.html#hepar2) - Number of nodes: 76 - Number of arcs: 112 - Number of parameters: 574 - Average…
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5000 instances - 76 features - classes - 0 missing values
bnlearn Bayesian Network Repository reference: [URL](https://www.bnlearn.com/bnrepository/discrete-verylarge.html#andes) - Number of nodes: 223 - Number of arcs: 338 - Number of parameters: 1157 -…
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5000 instances - 223 features - classes - 0 missing values
At Santander our mission is to help people and businesses prosper. We are always looking for ways to help our customers understand their financial health and identify which products and services might…
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200000 instances - 201 features - 2 classes - 0 missing values
Context "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets] Content Each row represents a…
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7043 instances - 20 features - 2 classes - 11 missing values
======================================================================================================== Seismic bumps dataset…
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2584 instances - 19 features - 2 classes - 0 missing values
Dota 2 is a popular computer game with two teams of 5 players. At the start of the game each player chooses a unique hero with different strengths and weaknesses. Source: stephen.tridgell '@'…
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102944 instances - 117 features - 2 classes - 0 missing values
This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using…
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32561 instances - 15 features - classes - 4262 missing values
Pulsar candidates collected during the HTRU survey. Pulsars are a type of star, of considerable scientific interest. Candidates must be classified in to pulsar and non-pulsar classes to aid discovery.…
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17898 instances - 9 features - 2 classes - 0 missing values
This is the training set of the COIL 2000 challenge as used by Huang et al. (2020). > Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z. (2020). > Tabtransformer: Tabular data modeling using…
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5822 instances - 86 features - 0 classes - 0 missing values
## Source: 1. C. Okan Sakar Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Bahcesehir University, 34349 Besiktas, Istanbul, Turkey 2. Yomi Kastro Inveon Information…
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12330 instances - 18 features - 2 classes - 0 missing values
## Overview The Otto Group is one of the world's biggest e-commerce companies, with subsidiaries in more than 20 countries, including Crate & Barrel (USA), Otto.de (Germany) and 3 Suisses (France). We…
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61878 instances - 94 features - 9 classes - 0 missing values
This is the datasets from the Kaggle Higgs Boson Machine Learning Challenge 2014. The data was downloaded from the [CERN website](http://opendata.cern.ch/record/328), which also hosts the…
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818238 instances - 31 features - 2 classes - 0 missing values
This is the datasets from the Kaggle Higgs Boson Machine Learning Challenge 2014. The data was downloaded from the [CERN website](http://opendata.cern.ch/record/328), which also hosts the…
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818238 instances - 31 features - 2 classes - 5168486 missing values
This dataset is from the "Explainable Machine Learning Challenge": > The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC…
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9871 instances - 24 features - 2 classes - 12643 missing values
This dataset is a subset of the [KDDCup 2012 track 2](https://www.kaggle.com/competitions/kddcup2012-track2/) data created by Manu Joseph and Harsh Raj for the paper > Joseph, M., & Raj, H. (2022). >…
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1000000 instances - 12 features - 2 classes - 0 missing values
Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to…
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961 instances - 5 features - 2 classes - 160 missing values
Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati) ---------------- This dataset is part of a series of five different datasets…
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2400 instances - 31 features - 0 classes - 0 missing values
Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.0GHz(Urbinati) ---------------- This dataset is part of a series of five different…
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2400 instances - 31 features - 0 classes - 0 missing values
Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.5GHz(Urbinati) ---------------- This dataset is part of a series of five different…
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2400 instances - 31 features - 0 classes - 0 missing values
Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati) ---------------- This dataset is part of a series of five different…
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2400 instances - 31 features - 0 classes - 0 missing values
A Tour & Travels Company Wants To Predict Whether A Customer Will Churn Or Not Based On Indicators Given Below. Help Build Predictive Models And Save The Company's Money. Perform Fascinating EDAs. The…
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954 instances - 7 features - 2 classes - 60 missing values
## Data description There are 3 types of input features: * Objective: factual information; * Examination: results of medical examination; * Subjective: information given by the patient. Features: 1.…
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70000 instances - 12 features - 2 classes - 0 missing values
This is the datasets from the Kaggle Higgs Boson Machine Learning Challenge 2014. The data was downloaded from the [CERN website](http://opendata.cern.ch/record/328), which also hosts the…
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800000 instances - 31 features - 2 classes - 5053446 missing values
This dataset is from the "Explainable Machine Learning Challenge": > The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC…
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9871 instances - 24 features - 2 classes - 0 missing values
DBLP-QuAD is a scholarly question answering dataset over the DBLP knowledge graph. The dataset can also be found at https://zenodo.org/record/7643971 and…
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10000 instances - 10 features - 9999 classes - 0 missing values
This is a classification problem to distinguish between a signal process which produces Higgs bosons and a background process which does not. ## Information The data has been produced using Monte…
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11000000 instances - 29 features - 2 classes - 0 missing values
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the…
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97852 instances - 7 features - classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/CAD from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/NZD from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1832 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX USD/DKK from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1832 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/CHF from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX AUD/CHF from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to…
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43825 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/HKD from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX CAD/JPY from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX CHF/JPY from Dukascopy. One instance (row) is one candlestick of one minute. The whole dataset has the data range from 1-1-2018 to…
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375840 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX USD/CHF from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1835 instances - 12 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
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14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
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14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
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14 instances - 5 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/JPY from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1832 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX EUR/USD from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1837 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX AUD/CAD from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to…
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1834 instances - 12 features - 2 classes - 0 missing values
Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Solar Flare dataset (Lichman 2013) has 3 target variables that correspond to the number of…
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323 instances - 13 features - classes - 0 missing values
Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Solar Flare dataset (Lichman 2013) has 3 target variables that correspond to the number of…
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1066 instances - 13 features - classes - 0 missing values
Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Concrete Slump dataset (Yeh 2007) concerns the prediction of three properties of concrete…
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103 instances - 10 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
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150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
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150 instances - 5 features - 3 classes - 0 missing values