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BankChurners (8)
Testing for trend in ds assignment
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BankChurners (7)
Testing for trend in ds assignment
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BankChurners (6)
Testing for trend in ds assignment
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BankChurners (5)
Testing for trend in ds assignment
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BankChurners (4)
Testing for trend in ds assignment
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BankChurners (3)
Testing for trend in ds assignment
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BankChurners (2)
Testing for trend in ds assignment
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BankChurners (1)
Testing for trend in ds assignment
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Weather_Beutenberg (1)
Weather measures from Beutenberg provided by the Max-Planck-Institute for Biogeochemistry Several weather measures provided by Max-Planck-Institute for Biogeochemistry from the Weather Station on Top…
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BTC (1)
### Description: The `bitstampUSD_1-min_data_2012-01-01_to_2021-03-31.csv` dataset is a comprehensive collection of minute-by-minute Bitcoin trading data on the Bitstamp platform, spanning from…
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4857377 instances - 8 features - classes - 8705256 missing values
drug_names (1)
Description: The "medicine_prescription_records.csv" dataset is a comprehensive collection of prescription patterns across various medical specialties. It records the prescription habits of medical…
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CrypAnalysis (1)
Analysis Crypt
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34497 instances - 8 features - classes - 0 missing values
Obesity (1)
Description: The dataset named "ObesityDataSet_raw_and_data_sinthetic.csv" comprises comprehensive attributes aimed at the analysis and prediction of obesity levels among individuals. It captures a…
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2111 instances - 17 features - classes - 0 missing values
BraidFlowSessionLevel (1)
Entering a cognitive state of flow is a natural response of the mind that allows people to fully concentrate and cope with tedious, and often repetitive tasks. Understanding how to trigger or sustain…
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2364 instances - 124 features - 3 classes - 0 missing values
BraidFlow (1)
Entering a cognitive state of flow is a natural response of the mind that allows people to fully concentrate and cope with tedious, and often repetitive tasks. Understanding how to trigger or sustain…
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72 instances - 124 features - 3 classes - 159 missing values
(Non-)depressive_tweet_data (2)
Description: The dataset, named "clean_tweet_Dec19ToDec20.csv," comprises a collection of tweets post-processed for clarity and analysis, spanning from December 2019 to December 2020. It is designed…
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134348 instances - 3 features - classes - 18 missing values
(Non-)depressive_tweet_data (1)
Description: The dataset, named "clean_tweet_Dec19ToDec20.csv," comprises a collection of tweets post-processed for clarity and analysis, spanning from December 2019 to December 2020. It is designed…
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134348 instances - 3 features - classes - 18 missing values
Trina (4)
Trina
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28389 instances - 9 features - classes - 0 missing values
Trina (3)
Trina
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28389 instances - 9 features - classes - 0 missing values
Trina (2)
Trina
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28389 instances - 9 features - classes - 0 missing values
Trina (1)
Trina
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28389 instances - 9 features - classes - 0 missing values
Laboratorio_dataset_car (3)
Set datos Actividad
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1750 instances - 7 features - classes - 0 missing values
Credit_Card_Fraud_ (1)
This dataset captures transaction patterns and behaviors that could indicate potential fraud in card transactions. The data is composed of several features designed to reflect the transactional…
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1000000 instances - 8 features - classes - 0 missing values
Crime_Data_from_2020_to_present_in_Los_Angeles (1)
Description: This dataset, named Crime_Data_from_2020_to_Present.csv, provides a detailed record of reported criminal incidents in a given area from the year 2020 onwards. It includes comprehensive…
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932140 instances - 28 features - classes - 5113856 missing values
Death_by_various_risk_factors (1)
Description: The "deathsbyriskfactor_new.csv" dataset encompasses a comprehensive collection of global health data focusing on deaths attributed to various risk factors across different countries. It…
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Linkedin_Job_Postings (1)
Description: The "postings.csv" dataset comprises various job postings across different companies and locations. It includes detailed information on job titles, job descriptions, salaries, and…
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123849 instances - 28 features - classes - 1133501 missing values
Lichess_Games_Dataset (1)
Description: The dataset games.csv is a comprehensive collection of chess game records, providing detailed insights into game outcomes, player ratings, moves, and opening strategies. It consists of…
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20058 instances - 16 features - classes - 0 missing values
Vinicius Alves 7 months ago
ran flow sklearn.pipeline.Pipeline(impu ... es.DecisionTreeClassifier)(33) on task Supervised Classification on data set pendigits
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9796, f_measure: 0.9634, kappa: 0.9593, kb_relative_information_score: 0.9617, mean_absolute_error: 0.0073, mean_prior_absolute_error: 0.18, weighted_recall: 0.9633, number_of_instances: 10992, precision: 0.9634, predictive_accuracy: 0.9633, prior_entropy: 3.3208, relative_absolute_error: 0.0407, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0856, root_relative_squared_error: 0.2855, unweighted_recall: 0.9633,
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (33)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
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sklearn.impute._base.SimpleImputer (57)
Univariate imputer for completing missing values with simple strategies. Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a…
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sklearn.tree._classes.DecisionTreeClassifier (49)
A decision tree classifier.
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Heart_Failure_Prediction (1)
Description: This dataset, named "heart_failure_clinical_records.csv", consists of clinical records of patients with heart failure. It includes various attributes such as age, anaemia, creatinine…
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5000 instances - 13 features - classes - 0 missing values
Heart_Failure_Prediction_-_Clinical_Records_ (1)
Description: The "heart_failure_clinical_records.csv" dataset comprises clinical records of patients with heart failure, detailing various medical attributes that may contribute to heart failure…
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5000 instances - 13 features - classes - 0 missing values
Electric_Vehicles (1)
Description: The Electric_Vehicle_Population_Data.csv dataset provides a comprehensive overview of the electric vehicle (EV) population within a specific region, highlighting key information about the…
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181458 instances - 17 features - classes - 421 missing values
Niro Tsou 7 months ago
ran flow sklearn.pipeline.Pipeline(impu ... es.DecisionTreeClassifier)(32) on task Supervised Classification on data set pendigits
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9795, f_measure: 0.9632, kappa: 0.9591, kb_relative_information_score: 0.9615, mean_absolute_error: 0.0074, mean_prior_absolute_error: 0.18, weighted_recall: 0.9632, number_of_instances: 10992, precision: 0.9632, predictive_accuracy: 0.9632, prior_entropy: 3.3208, relative_absolute_error: 0.0409, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.0858, root_relative_squared_error: 0.2862, unweighted_recall: 0.9632,
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (32)
A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…
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sklearn.impute._base.SimpleImputer (56)
Univariate imputer for completing missing values with simple strategies. Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a…
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sklearn.tree._classes.DecisionTreeClassifier (48)
A decision tree classifier.
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Jeepneys (3)
Intra
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204 instances - 22 features - classes - 1473 missing values
Jeepneys (2)
Intra
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204 instances - 22 features - classes - 1473 missing values
Jeepneys (1)
Intra
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204 instances - 22 features - classes - 1473 missing values
None (1)
Description: This dataset "Taxi_Trips_-_2024_20240408.csv" contains information on taxi trips in Chicago starting from February 2024. The dataset includes trip ID, taxi ID, trip start and end…
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865247 instances - 23 features - classes - 1514769 missing values
Soheila Farokhi 7 months ago
ran flow sklearn.pipeline.Pipeline(Prep ... est.RandomForestClassifier)(5) on task Supervised Classification on data set credit-g
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7562, f_measure: 0.7228, kappa: 0.3122, kb_relative_information_score: 0.1907, mean_absolute_error: 0.3347, mean_prior_absolute_error: 0.4202, weighted_recall: 0.748, number_of_instances: 1000, precision: 0.7314, predictive_accuracy: 0.748, prior_entropy: 0.8813, relative_absolute_error: 0.7966, root_mean_prior_squared_error: 0.4583, root_mean_squared_error: 0.4166, root_relative_squared_error: 0.9092, unweighted_recall: 0.6362,
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (5)
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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sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer) (6)
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
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sklearn.ensemble._forest.RandomForestClassifier (43)
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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Taniya D 7 months ago
ran flow torch.nn.Model2.a524e7d6f2f5accd(1) on task Supervised Classification on data set IndoorScenes
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4999, kappa: 0.0008, kb_relative_information_score: 0.0467, mean_absolute_error: 0.0294, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0307, number_of_instances: 15620, predictive_accuracy: 0.0307, prior_entropy: 5.7539, relative_absolute_error: 1.0075, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1212, root_relative_squared_error: 1.0039, unweighted_recall: 0.0155,
torch.nn.InvertedResidual.d22dd78f84c40331 (1)
Automatically created pytorch flow.
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torch.nn.Sequential.1feda6ba089730b (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.7d8d8dc642ed62dc (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.456a650dc0562c5e (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.d743748d114b4f7e (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.61ba1f2cab2a09f4 (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.3eb4665cc8da968f (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.d59817fc56f92fc4 (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.191befbfe9063fcf (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.44fb1cc811031fc9 (1)
Automatically created pytorch flow.
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torch.nn.InvertedResidual.e9523a34ff3f8168 (1)
Automatically created pytorch flow.
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torch.nn.Sequential.ae567afc3109e376 (1)
Automatically created pytorch flow.
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torch.nn.Sequential.dbfe2cf7f89b11ad (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.3d8f7a9ec382ec2e (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.c1be8b49907a3e61 (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.5802bd5ea4a59aff (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.bc38d1657157ffee (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.350eee6951fd92eb (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.f13c2b3649629e8c (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.574190a6ede0d7af (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.3ff1d31fb345daba (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.a8a4e41b5565ea3f (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.6f99b87d9914ae79 (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.3ca8176f25303155 (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.9fb1a0b175a6b99b (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.b40b5e7a895cf910 (1)
Automatically created pytorch flow.
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torch.nn.InvertedResidual.b3eb152be85d5575 (1)
Automatically created pytorch flow.
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torch.nn.Sequential.9ee44b0b616b8f5 (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.4c3a1708a930a895 (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.a886fb3b16da161 (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.bc110042fbb6a1fa (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.48ce98e6cf2dfdb2 (1)
Automatically created pytorch flow.
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torch.nn.InvertedResidual.87560cb09670e0df (1)
Automatically created pytorch flow.
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torch.nn.Sequential.3dc46e015fabc6bf (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.859c92051ff3efc4 (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.1e08e4b35f6e2295 (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.9f812263897f2cdf (1)
Automatically created pytorch flow.
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torch.nn.InvertedResidual.4f05d2363de136fb (1)
Automatically created pytorch flow.
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torch.nn.Sequential.504b430bab24f666 (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.53e44296e069eb9c (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.5bd85730405f2e60 (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.5da6c4321903fbd3 (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.1211121d1916a3d6 (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.9d3f7d1bc52a8930 (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.b7f5ae9020afca80 (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.22e66080535a5381 (1)
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torch.nn.Conv2d.35f4812f780f75d3 (1)
Automatically created pytorch flow.
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torch.nn.BatchNorm2d.99331f707e316ef9 (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.e162b216f2453abd (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.dbaf726aa4fe9f46 (1)
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torch.nn.BatchNorm2d.88572129d04cd216 (1)
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torch.nn.Conv2d.c00f0fa89c45cac8 (1)
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torch.nn.BatchNorm2d.e379caa347d56f8e (1)
Automatically created pytorch flow.
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