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 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 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 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
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
======================================================================================================== 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
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 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
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
## 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 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
mki
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8 instances - 2 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 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
# Data Description This is the historical price data of the FOREX USD/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 AUD/JPY 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/HUF 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 AUD/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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1834 instances - 12 features - 2 classes - 0 missing values
# Data Description This is the historical price data of the FOREX USD/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
Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the…
0 runs0 likes1 downloads1 reach11 impact
70000 instances - 785 features - 10 classes - 0 missing values
test
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150 instances - 5 features - classes - 0 missing values
Binarized version of the USPS dataset (see version 2). Only instances with class labels 6 and 9 from the original dataset are considered and encoded as 0 (original class 6) and 1 (original class 9).
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1424 instances - 257 features - 2 classes - 0 missing values
Binarized version of the isolet dataset (see version 1). Only instances with class labels 1 and 2 from the original dataset are considered.
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600 instances - 618 features - 2 classes - 0 missing values
Binarized version of the cnae-9 dataset (see version 1). Only instances with class labels 1 and 2 from the original dataset are considered.
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240 instances - 857 features - 2 classes - 0 missing values
testtest
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1994 instances - 127 features - 0 classes - 0 missing values
This collection includes 21 data sets of one-dimensional ultrasound raw RF data (A-Scans) acquired from the calf muscles of 8 healthy volunteers. The subjects were asked to manually annotate the data…
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212872 instances - 4 features - classes - 0 missing values
Data contains the information of 9144 samples form 220 spectral bands. The classes represent land-use types: alfalfa, corn, grass, hay, oats, soybeans, trees, and wheat.
0 runs0 likes2 downloads2 reach11 impact
9144 instances - 221 features - 8 classes - 0 missing values
Binarized version of the semeion dataset (see version 1). Only instances with class labels 1 and 2 from the original dataset are considered.
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319 instances - 257 features - 2 classes - 0 missing values
This is a meta-dataset which describes the SVM hyperparameter tuning problem. The target attribute indicates whether tuning is required or default hyperparameter values are enough to each dataset…
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156 instances - 81 features - 2 classes - 0 missing values
This is a meta-dataset which describes the SVM hyperparameter tuning problem. The target attribute indicates whether tuning is required or default hyperparameter values are enough to each dataset…
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156 instances - 91 features - 2 classes - 0 missing values
This is a meta-dataset which describes the SVM hyperparameter tuning problem. The target attribute indicates whether tuning is required or default hyperparameter values are enough to each dataset…
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156 instances - 81 features - 2 classes - 0 missing values
test
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150 instances - 5 features - classes - 0 missing values
The ILPD liver dataset from the OpenCC18 with the gender binary encoded so all features are numeric
1 runs0 likes0 downloads0 reach0 impact
583 instances - 11 features - 2 classes - 0 missing values
Sick dataset from the opencc18 with all textual binary variables label encoded.
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3772 instances - 30 features - 2 classes - 0 missing values