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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : target
This dataset contains Apple stock prices from 2014 to 2023, along with various technical indicators. It can be used for multi-class classification to predict the price trend for the next day based on…
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2516 instances - 19 features - 3 classes - 0 missing values
This dataset contains information about credit card default payments in Taiwan. The target variable indicates whether a customer will default on their payment next month. Target classes:…
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30001 instances - 25 features - 1 classes - 30001 missing values
This dataset contains information about credit card default payments in Taiwan. The target variable indicates whether a customer will default on their payment next month. Target classes:…
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30001 instances - 25 features - 1 classes - 30001 missing values
The dataset contains customers' default payments in Taiwan. The target variable 'def_next_mo' is now treated as nominal with categories: default_payment, no_default_payment.
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30001 instances - 25 features - 1 classes - 30001 missing values
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uploader_id : 45575 - estimation_procedure : 10-fold Crossvalidation - target_feature : creditability
Dataset is uploaded from kaggle, see citation for the link.
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1000 instances - 21 features - 2 classes - 0 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
This dataset collects 200+ financial indicators for all the stocks of the US stock market.. The target variable 'class' is now treated as nominal with categories: stock_value_increase,…
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4392 instances - 222 features - 2 classes - 93949 missing values
This dataset collects 200+ financial indicators for all the stocks of the US stock market.. The target variable 'class' is now treated as nominal with categories: stock_value_increase,…
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4392 instances - 222 features - 2 classes - 96995 missing values
This dataset collects 200+ financial indicators for all the stocks of the US stock market.. The target variable 'class' is now treated as nominal with categories: stock_value_increase,…
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41683 instances - 31 features - 2 classes - 136 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : loan_status
An updated version of the dataset for loan status category. The target variable 'loan_status' is now treated as nominal with categories: loan aprproved, loan rejected.
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45000 instances - 14 features - 2 classes - 0 missing values
An updated version of the dataset for loan status category. The target variable 'loan_status' is now treated as nominal with categories: loan aprproved, loan rejected.
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45000 instances - 14 features - 2 classes - 45000 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
The dataset contains transactions made by credit cards in September 2013 by European cardholders. The target variable 'class' is now treated as nominal with categories: fraud, otherwise.
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41683 instances - 31 features - 3 classes - 28 missing values
The dataset contains transactions made by credit cards in September 2013 by European cardholders. The target variable 'class' is now treated as nominal with categories: fraud, otherwise.
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41683 instances - 31 features - 3 classes - 28 missing values
The dataset contains transactions made by credit cards in September 2013 by European cardholders. The target variable 'class' is now treated as nominal with categories: fraud, otherwise.
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41683 instances - 31 features - 3 classes - 28 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : loan_status
An updated version of the dataset for loan status category. The target variable 'loan_status' is now treated as nominal with categories: loan aprproved, loan rejected.
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45000 instances - 14 features - 2 classes - 45000 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : loan_status
An updated version of the dataset for loan status category. The target variable 'loan_status' is now treated as nominal with categories: loan aprproved, loan rejected.
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45000 instances - 14 features - 1 classes - 90000 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : target
Apple Price Trend prediction. The target variable 'target' is a categorical variable with 3 classes: bearish (0), bullish (1), and neutral (2). This is a multiclass classification task.
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2516 instances - 23 features - 0 classes - 0 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : target
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : bad_flag
Apple Price Trend prediction. The target variable 'target' is a categorical variable with 3 classes: bearish (0), bullish (1), and neutral (2). This is a multiclass classification task.
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2516 instances - 23 features - 0 classes - 0 missing values
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uploader_id : 45956 - estimation_procedure : 10-fold Crossvalidation - target_feature : bad_flag
Apple Price Trend prediction. The target variable 'target' is now treated as nominal with categories: bearish, bullish, and neutral.
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2516 instances - 23 features - 0 classes - 0 missing values
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : total_score
This dataset is designed to evaluate companies based on quality and valuation metrics. It uses a two-stage scoring system to classify companies into categories like 'High Quality, Fair Valuation'. The…
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719 instances - 11 features - 6 classes - 0 missing values
cleaned Soccer DB
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3260 instances - 27 features - classes - 0 missing values
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uploader_id : 44331 - estimation_procedure : 10-fold Crossvalidation - target_feature : application.accepted
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uploader_id : 44331 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
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uploader_id : 44331 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
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uploader_id : 44331 - estimation_procedure : 10-fold Crossvalidation - target_feature : seriousdlqin2yrs
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uploader_id : 44331 - estimation_procedure : 10-fold Crossvalidation - target_feature : y
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6 instances - 3 features - classes - 0 missing values
Finantial dataset for automl benchmark. Dataset 461 with target column application.accepted
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100 instances - 7 features - 2 classes - 0 missing values
Finantial dataset for automl benchmark. Dataset 31 with target column class
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1000 instances - 21 features - 2 classes - 0 missing values
Finantial dataset for automl benchmark. Dataset 29 with target column class
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690 instances - 16 features - 2 classes - 55 missing values
Finantial dataset for automl benchmark. Dataset 44089 with target column seriousdlqin2yrs
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16714 instances - 11 features - 2 classes - 0 missing values
Finantial dataset for automl benchmark. Dataset 42477 with target column y
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30000 instances - 24 features - 2 classes - 0 missing values
Finantial dataset for automl benchmark. Dataset 42477 with target column y
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30000 instances - 24 features - 2 classes - 0 missing values
Finantial dataset for automl benchmark. Dataset 42477 with target column y
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30000 instances - 24 features - 2 classes - 0 missing values
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uploader_id : 44331 - estimation_procedure : 10-fold Crossvalidation - target_feature : Application.accepted
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : is_fraud
This dataset is derived from the Credit Risk Analytics book by Harald, Daniel, and Bart, as described in the Medium article by Roi Polanitzer. It focuses on predicting financial difficulties and…
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5000 instances - 8 features - 10 classes - 0 missing values
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : is_fraud
An updated version of the Customer Churn dataset for binary classification. The target variable 'exited' is now treated as nominal with categories: 'not_churned' and 'churned'.
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35000 instances - 25 features - 2 classes - 0 missing values
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : is_fraud
A fraud detection dataset for binary classification. The target variable is 'is_fraud', indicating whether a transaction is fraudulent.
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5227 instances - 22 features - 2 classes - 0 missing values
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : class
A dataset for binary classification of credit approval status. Features include customer demographics, financial attributes, and credit history. The target variable `class` indicates whether the…
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1000 instances - 21 features - 2 classes - 0 missing values
This dataset contains customer data for a classification task to predict churn based on behavioral and demographic features. The target variable 'exited' indicates whether a customer has churned (1)…
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175028 instances - 25 features - 2 classes - 0 missing values
This dataset contains customer data for a classification task to predict churn based on behavioral and demographic features. The target variable 'exited' indicates whether a customer has churned (1)…
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175028 instances - 25 features - 2 classes - 0 missing values
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : anomaly
An updated version of the dataset for classifying risk levels in transactions. The target variable 'anomaly' is now treated as nominal with categories: low risk, moderate risk, and high risk.
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78600 instances - 19 features - 3 classes - 0 missing values
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : Credit_Score
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6058, kappa: 0.0221, kb_relative_information_score: 0.0816, mean_absolute_error: 0.0288, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0538, number_of_instances: 15620, predictive_accuracy: 0.0538, prior_entropy: 5.7539, relative_absolute_error: 0.9878, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1276, root_relative_squared_error: 1.0561, unweighted_recall: 0.0253,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5933, kappa: 0.0169, kb_relative_information_score: 0.0802, mean_absolute_error: 0.0289, mean_prior_absolute_error: 0.0292, weighted_recall: 0.044, number_of_instances: 15620, predictive_accuracy: 0.044, prior_entropy: 5.7539, relative_absolute_error: 0.9902, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1257, root_relative_squared_error: 1.0406, unweighted_recall: 0.0243,
Automatically created tensorflow flow.
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5232, kappa: 0.0027, kb_relative_information_score: 0.0535, mean_absolute_error: 0.0293, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0214, number_of_instances: 15620, predictive_accuracy: 0.0214, prior_entropy: 5.7539, relative_absolute_error: 1.0049, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1238, root_relative_squared_error: 1.0251, unweighted_recall: 0.014,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6259, kappa: 0.035, kb_relative_information_score: 0.0935, mean_absolute_error: 0.0287, mean_prior_absolute_error: 0.0292, weighted_recall: 0.068, number_of_instances: 15620, predictive_accuracy: 0.068, prior_entropy: 5.7539, relative_absolute_error: 0.9841, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.124, root_relative_squared_error: 1.027, unweighted_recall: 0.0309,
Automatically created tensorflow flow.
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5268, kappa: 0.0106, kb_relative_information_score: 0.0544, mean_absolute_error: 0.0293, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0255, number_of_instances: 15620, predictive_accuracy: 0.0255, prior_entropy: 5.7539, relative_absolute_error: 1.0034, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1239, root_relative_squared_error: 1.0263, unweighted_recall: 0.019,
Automatically created tensorflow flow.
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5289, kappa: 0.0083, kb_relative_information_score: 0.0505, mean_absolute_error: 0.0293, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0198, number_of_instances: 15620, predictive_accuracy: 0.0198, prior_entropy: 5.7539, relative_absolute_error: 1.0057, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1244, root_relative_squared_error: 1.0299, unweighted_recall: 0.0218,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5261, kappa: 0.004, kb_relative_information_score: 0.0513, mean_absolute_error: 0.0294, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0169, number_of_instances: 15620, predictive_accuracy: 0.0169, prior_entropy: 5.7539, relative_absolute_error: 1.0064, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.123, root_relative_squared_error: 1.0184, unweighted_recall: 0.0175,
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uploader_id : 44101 - estimation_procedure : 33% Holdout set - target_feature : bad_flag
Updated Fraud Detection dataset with nominal target for binary classification.
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4156 instances - 29 features - 2 classes - 25715 missing values
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uploader_id : 44101 - estimation_procedure : 33% Holdout set - target_feature : bad_flag
Updated Fraud Detection dataset with nominal target for binary classification.
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4156 instances - 29 features - 2 classes - 25715 missing values
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uploader_id : 44101 - estimation_procedure : 33% Holdout set - target_feature : class
A fraud detection dataset with date components and categorical encodings for OpenML upload.
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4156 instances - 29 features - 0 classes - 25715 missing values
German credit dataset, similar to original one, just age and gender are two different attributes. More info on the dataset here: https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data
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1000 instances - 22 features - 0 classes - 0 missing values
Prediction of CRISPR-Cas9 off-target based on effective use of sequence information
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217733 instances - 7 features - classes - 0 missing values
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9372, f_measure: 0.8807, kappa: 0.7352, kb_relative_information_score: 0.5991, mean_absolute_error: 0.1934, mean_prior_absolute_error: 0.456, weighted_recall: 0.8826, number_of_instances: 19020, precision: 0.8825, predictive_accuracy: 0.8826, prior_entropy: 0.9355, relative_absolute_error: 0.4242, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2983, root_relative_squared_error: 0.6246, unweighted_recall: 0.8573,
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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classification
24 datasets, 24 tasks, 0 flows, 0 runs
classification
24 datasets, 24 tasks, 0 flows, 0 runs
classification
24 datasets, 24 tasks, 0 flows, 0 runs
A custom suite containing specific OpenML tasks for benchmarking.
24 datasets, 24 tasks, 0 flows, 0 runs
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem
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uploader_id : 44101 - estimation_procedure : 10-fold Crossvalidation - target_feature : classification problem