0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9785, f_measure: 0.9613, kappa: 0.957, kb_relative_information_score: 0.9596, mean_absolute_error: 0.0077, mean_prior_absolute_error: 0.18, weighted_recall: 0.9613, number_of_instances: 10992, precision: 0.9614, predictive_accuracy: 0.9613, prior_entropy: 3.3208, relative_absolute_error: 0.043, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0879, root_relative_squared_error: 0.2931, unweighted_recall: 0.9614,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5227, f_measure: 0.5486, kappa: 0.0507, kb_relative_information_score: 0.1682, mean_absolute_error: 0.3708, mean_prior_absolute_error: 0.456, weighted_recall: 0.6569, number_of_instances: 19020, precision: 0.6482, predictive_accuracy: 0.6569, prior_entropy: 0.9355, relative_absolute_error: 0.8133, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.547, root_relative_squared_error: 1.1455, unweighted_recall: 0.5201,
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features.…
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Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9359, f_measure: 0.8805, kappa: 0.7348, kb_relative_information_score: 0.5968, mean_absolute_error: 0.1946, mean_prior_absolute_error: 0.456, weighted_recall: 0.8824, number_of_instances: 19020, precision: 0.8821, predictive_accuracy: 0.8824, prior_entropy: 0.9355, relative_absolute_error: 0.4268, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2994, root_relative_squared_error: 0.6271, unweighted_recall: 0.8574,
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Churn
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : TARGET
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : target
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : income
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
Home Credit Default Risk Main Table > Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z. (2020). > Tabtransformer: Tabular data modeling using contextual embeddings. > arXiv preprint…
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307511 instances - 121 features - 2 classes - 9152465 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
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
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : CARAVAN
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : income
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Team_won
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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
======================================================================================================== Seismic bumps dataset…
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2584 instances - 19 features - 2 classes - 0 missing values
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Revenue
## 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
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
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
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : num
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Survival_status
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Type
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : surgical_lesion
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Severity
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : target
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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
digital_text
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digital_text
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digital_text
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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
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : label
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : target
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : two_year_recid
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Target
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : RiskPerformance
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Label
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 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
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 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
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Label
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Churn
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : binaryClass
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : target
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : y
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : cardio
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class_number_of_rings
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : V42
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : click
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : Class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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uploader_id : 86 - estimation_procedure : 4-fold Crossvalidation - target_feature : class
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
## 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
## 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
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
Inclusion Criteria: * There are between 500 and 100000 observations. * There are less than 5000 features after one-hot encoding all categorical features. * The dataset is not in a sparse format. * The…
35 datasets, 35 tasks, 0 flows, 0 runs
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
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-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-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-9.0GHz(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
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-21 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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9990 instances - 125 features - 11 classes - 0 missing values
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-20 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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19992 instances - 125 features - 11 classes - 0 missing values
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-19 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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9999 instances - 125 features - 11 classes - 0 missing values
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-18 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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9997 instances - 125 features - 11 classes - 0 missing values
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-15 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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19991 instances - 125 features - 11 classes - 0 missing values
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-14 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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19986 instances - 125 features - 11 classes - 0 missing values
Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-13 Attribute Descriptions: CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement…
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19988 instances - 125 features - 11 classes - 0 missing values