OpenML

torch.nn.modules.linear.Linear.aefd12c46381f6fd (1)

Automatically created pytorch flow.

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torch.nn.modules.linear.Linear.9c1953b126e3ca5c (1)

Automatically created pytorch flow.

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torch.nn.modules.linear.Linear.37b3e539f198f9c3 (1)

Automatically created pytorch flow.

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torch.nn.modules.pooling.MaxPool2d.635cd3268602baf3 (1)

Automatically created pytorch flow.

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Benchmark: Bleichenbacher Attack Training Datasets - Padding and Timing Leakage

This collection complements the Padding Attacks benchmark datasets serves as a valuable benchmark training set for multi-class classification tasks or detecting information leakage via error code or…

96 datasets, 96 tasks, 0 flows, 0 runs

Supervised Classification on padding-attack-dataset-panos-2023-12-13

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-netscalergcm-2023-12-13

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-facebook-2023-12-05

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-cisco-2023-12-04

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-OpenSSL111t-2023-04-28

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-OpenSSL097b-2023-04-28

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-OpenSSL097a-2023-04-28

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-DamnVulnerableOpenSSL-2023-04-28

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-OpenSSL-1.1.1k-2021-07-23

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

padding-attack-dataset-panos-2023-12-13 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-12-13 with server panos Attribute Names: CCS0:tcp.srcport CCS0:tcp.dstport CCS0:tcp.port CCS0:tcp.stream CCS0:tcp.len CCS0:tcp.seq…

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7869 instances - 197 features - 11 classes - 612770 missing values

padding-attack-dataset-netscalergcm-2023-12-13 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-12-13 with server netscalergcm Attribute Names: TLS0:tcp.srcport TLS0:tcp.dstport TLS0:tcp.port TLS0:tcp.stream TLS0:tcp.len TLS0:tcp.seq…

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7678 instances - 253 features - 11 classes - 1195562 missing values

padding-attack-dataset-facebook-2023-12-05 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-12-05 with server facebook Attribute Names: CKE0:tcp.srcport CKE0:tcp.dstport CKE0:tcp.port CKE0:tcp.stream CKE0:tcp.len CKE0:tcp.seq…

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19487 instances - 181 features - 11 classes - 1750620 missing values

padding-attack-dataset-cisco-2023-12-04 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-12-04 with server cisco Attribute Names: CCS0:tcp.srcport CCS0:tcp.dstport CCS0:tcp.port CCS0:tcp.stream CCS0:tcp.len CCS0:tcp.seq…

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7714 instances - 253 features - 11 classes - 1157656 missing values

padding-attack-dataset-OpenSSL111t-2023-04-28 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-04-28 with server OpenSSL111t Attribute Names: TLS0:tcp.srcport TLS0:tcp.dstport TLS0:tcp.port TLS0:tcp.stream TLS0:tcp.len TLS0:tcp.seq…

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219900 instances - 95 features - 11 classes - 0 missing values

padding-attack-dataset-OpenSSL097b-2023-04-28 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-04-28 with server OpenSSL097b Attribute Names: TLS0:tcp.srcport TLS0:tcp.dstport TLS0:tcp.port TLS0:tcp.stream TLS0:tcp.len TLS0:tcp.seq…

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219915 instances - 125 features - 11 classes - 6597420 missing values

padding-attack-dataset-OpenSSL097a-2023-04-28 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-04-28 with server OpenSSL097a Attribute Names: TLS0:tcp.srcport TLS0:tcp.dstport TLS0:tcp.port TLS0:tcp.stream TLS0:tcp.len TLS0:tcp.seq…

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219913 instances - 125 features - 11 classes - 6597360 missing values

padding-attack-dataset-DamnVulnerableOpenSSL-2023-04-28 (1)

Bleichenbacher Padding Attack: Dataset created on 2023-04-28 with server DamnVulnerableOpenSSL Attribute Names: TLS0:tcp.srcport TLS0:tcp.dstport TLS0:tcp.port TLS0:tcp.stream TLS0:tcp.len…

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219920 instances - 95 features - 11 classes - 0 missing values

padding-attack-dataset-OpenSSL-1.1.1k-2021-07-23 (1)

Bleichenbacher Padding Attack: Dataset created on 2021-07-23 with server OpenSSL-1.1.1k Attribute Names: TLS0:tcp.srcport TLS0:tcp.dstport TLS0:tcp.port TLS0:tcp.stream TLS0:tcp.len TLS0:tcp.seq…

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49977 instances - 117 features - 11 classes - 2196060 missing values

Supervised Classification on padding-attack-dataset-2023-04-28-OpenSSL097a-2023-04-28

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-2023-04-28-DamnVulnerableOpenSSL-2023-04-28

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Supervised Classification on padding-attack-dataset-2021-07-23-OpenSSL-1.1.1k-2021-07-23

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uploader_id : 2086 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label

Amphibian (1)

amphibians

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189 instances - 16 features - 0 classes - 0 missing values

algae (1)

This dataset contains 340 instances concerning the frequencies of seven types of algae populations in different environments.

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316 instances - 25 features - classes - 0 missing values

DrinkDataset (1)

fake dataset without any value

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73503 instances - 4 features - classes - 0 missing values

StudentsPerformance (2)

This data set consists of the marks secured by the students in various subjects.

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1000 instances - 8 features - 2 classes - 0 missing values

test (16)

test

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73503 instances - 4 features - classes - 0 missing values

cardiotocography (5)

The dataset consists of measurements of fetal heart rate and uterine contraction features on cardiotocograms classified by expert obstetricians.

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2126 instances - 33 features - classes - 0 missing values

TestDatasetOfLocations (1)

Test dataset to see upload.

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73503 instances - 4 features - 2 classes - 0 missing values

CookbookReviews (1)

The "Cookbook Reviews" is an extensive data set that includes a range of information about user interactions and recipe reviews. It contains important details like the recipe name, where it stands in…

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18182 instances - 14 features - 0 classes - 2 missing values

covertype (16)

Predicting forest cover ...

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18182 instances - 14 features - 0 classes - 2 missing values

covertype (15)

Predicting forest cover ...

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18182 instances - 14 features - 0 classes - 2 missing values

beans (1)

This dataset aims to distinguish seven different types of dry beans, taking into account the features such as form, shape, type, and structure by the market situation.

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13611 instances - 23 features - classes - 0 missing values

covertype (14)

Predicting forest cover ...

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73503 instances - 4 features - 2 classes - 0 missing values

movies (3)

This dataset includes demographic data for users who have rated the top 15 most-rated movies, ranked based on a star rating system.

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260 instances - 79 features - classes - 0 missing values

yeast (12)

This dataset consists of predicting the cellular localization sites of proteins.

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1484 instances - 18 features - classes - 0 missing values

libras (3)

The dataset contains 15 classes of 24 instances each, where each class references to a hand movement type in libras.

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360 instances - 105 features - classes - 0 missing values

movies (2)

This dataset includes demographic data for users who have rated the top 15 most-rated movies, ranked based on a star rating system.

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260 instances - 79 features - classes - 0 missing values

sushi (1)

This dataset is composed of demographic data about 5000 people and their sushi preferences.

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5000 instances - 136 features - classes - 0 missing values

MABL (1)

A brief description of your dataset.

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3 instances - 3 features - 3 classes - 0 missing values

letter (4)

The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet.

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20000 instances - 42 features - classes - 0 missing values

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set mnist_784

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6384, kappa: 0.1024, kb_relative_information_score: 0.1397, mean_absolute_error: 0.172, mean_prior_absolute_error: 0.1799, weighted_recall: 0.1986, number_of_instances: 70000, predictive_accuracy: 0.1986, prior_entropy: 3.3198, relative_absolute_error: 0.9558, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2933, root_relative_squared_error: 0.9777, unweighted_recall: 0.1833,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set analcatdata_dmft

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5219, kappa: 0.0289, kb_relative_information_score: 0.0432, mean_absolute_error: 0.2757, mean_prior_absolute_error: 0.2774, weighted_recall: 0.1882, number_of_instances: 797, predictive_accuracy: 0.1882, prior_entropy: 2.5803, relative_absolute_error: 0.994, root_mean_prior_squared_error: 0.3724, root_mean_squared_error: 0.3717, root_relative_squared_error: 0.9981, unweighted_recall: 0.1924,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set analcatdata_authorship

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.773, kappa: 0.4341, kb_relative_information_score: 0.3574, mean_absolute_error: 0.2447, mean_prior_absolute_error: 0.3439, weighted_recall: 0.6421, number_of_instances: 841, predictive_accuracy: 0.6421, prior_entropy: 1.7874, relative_absolute_error: 0.7116, root_mean_prior_squared_error: 0.4146, root_mean_squared_error: 0.355, root_relative_squared_error: 0.8562, unweighted_recall: 0.44,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set isolet

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6231, kappa: 0.0397, kb_relative_information_score: 0.1224, mean_absolute_error: 0.0713, mean_prior_absolute_error: 0.074, weighted_recall: 0.0767, number_of_instances: 7797, predictive_accuracy: 0.0767, prior_entropy: 4.7004, relative_absolute_error: 0.9637, root_mean_prior_squared_error: 0.1923, root_mean_squared_error: 0.1888, root_relative_squared_error: 0.9817, unweighted_recall: 0.0767,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set vowel

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4849, kappa: -0.0844, kb_relative_information_score: 0.0305, mean_absolute_error: 0.1668, mean_prior_absolute_error: 0.1653, weighted_recall: 0.0141, number_of_instances: 990, predictive_accuracy: 0.0141, prior_entropy: 3.4594, relative_absolute_error: 1.0094, root_mean_prior_squared_error: 0.2875, root_mean_squared_error: 0.2992, root_relative_squared_error: 1.0408, unweighted_recall: 0.0141,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set sick

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9141, f_measure: 0.9679, kappa: 0.742, kb_relative_information_score: 0.4961, mean_absolute_error: 0.0502, mean_prior_absolute_error: 0.1152, weighted_recall: 0.9655, number_of_instances: 3772, precision: 0.9725, predictive_accuracy: 0.9655, prior_entropy: 0.3324, relative_absolute_error: 0.4355, root_mean_prior_squared_error: 0.2398, root_mean_squared_error: 0.1587, root_relative_squared_error: 0.6617, unweighted_recall: 0.9311,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set eucalyptus

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6867, kappa: 0.2488, kb_relative_information_score: 0.2595, mean_absolute_error: 0.2709, mean_prior_absolute_error: 0.3132, weighted_recall: 0.4457, number_of_instances: 736, predictive_accuracy: 0.4457, prior_entropy: 2.2621, relative_absolute_error: 0.8649, root_mean_prior_squared_error: 0.3957, root_mean_squared_error: 0.3682, root_relative_squared_error: 0.9304, unweighted_recall: 0.338,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set satimage

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6879, kappa: 0.2525, kb_relative_information_score: 0.2491, mean_absolute_error: 0.2207, mean_prior_absolute_error: 0.2701, weighted_recall: 0.4257, number_of_instances: 6430, predictive_accuracy: 0.4257, prior_entropy: 2.4834, relative_absolute_error: 0.8171, root_mean_prior_squared_error: 0.3675, root_mean_squared_error: 0.3327, root_relative_squared_error: 0.9052, unweighted_recall: 0.3153,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set electricity

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7283, f_measure: 0.7486, kappa: 0.484, kb_relative_information_score: 0.2912, mean_absolute_error: 0.3659, mean_prior_absolute_error: 0.4886, weighted_recall: 0.7574, number_of_instances: 45312, precision: 0.7651, predictive_accuracy: 0.7574, prior_entropy: 0.9835, relative_absolute_error: 0.7488, root_mean_prior_squared_error: 0.4943, root_mean_squared_error: 0.4277, root_relative_squared_error: 0.8653, unweighted_recall: 0.7328,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set vehicle

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6416, kappa: 0.1935, kb_relative_information_score: 0.1955, mean_absolute_error: 0.3386, mean_prior_absolute_error: 0.3748, weighted_recall: 0.3901, number_of_instances: 846, predictive_accuracy: 0.3901, prior_entropy: 1.9991, relative_absolute_error: 0.9033, root_mean_prior_squared_error: 0.4329, root_mean_squared_error: 0.4124, root_relative_squared_error: 0.9526, unweighted_recall: 0.4022,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set tic-tac-toe

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6491, f_measure: 0.7002, kappa: 0.34, kb_relative_information_score: 0.1265, mean_absolute_error: 0.4008, mean_prior_absolute_error: 0.453, weighted_recall: 0.6994, number_of_instances: 958, precision: 0.7011, predictive_accuracy: 0.6994, prior_entropy: 0.931, relative_absolute_error: 0.8847, root_mean_prior_squared_error: 0.4759, root_mean_squared_error: 0.4479, root_relative_squared_error: 0.9411, unweighted_recall: 0.6709,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set splice

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7631, kappa: 0.4122, kb_relative_information_score: 0.334, mean_absolute_error: 0.3013, mean_prior_absolute_error: 0.4101, weighted_recall: 0.6238, number_of_instances: 3190, predictive_accuracy: 0.6238, prior_entropy: 1.4802, relative_absolute_error: 0.7348, root_mean_prior_squared_error: 0.4528, root_mean_squared_error: 0.3882, root_relative_squared_error: 0.8574, unweighted_recall: 0.579,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set spambase

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7762, f_measure: 0.7754, kappa: 0.5248, kb_relative_information_score: 0.3459, mean_absolute_error: 0.3297, mean_prior_absolute_error: 0.4776, weighted_recall: 0.7798, number_of_instances: 4601, precision: 0.7784, predictive_accuracy: 0.7798, prior_entropy: 0.9674, relative_absolute_error: 0.6904, root_mean_prior_squared_error: 0.4886, root_mean_squared_error: 0.4116, root_relative_squared_error: 0.8424, unweighted_recall: 0.7546,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set diabetes

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6785, f_measure: 0.7133, kappa: 0.3621, kb_relative_information_score: 0.1738, mean_absolute_error: 0.3832, mean_prior_absolute_error: 0.4545, weighted_recall: 0.7174, number_of_instances: 768, precision: 0.7114, predictive_accuracy: 0.7174, prior_entropy: 0.9331, relative_absolute_error: 0.843, root_mean_prior_squared_error: 0.4766, root_mean_squared_error: 0.446, root_relative_squared_error: 0.9357, unweighted_recall: 0.6765,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set pendigits

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6747, kappa: 0.1105, kb_relative_information_score: 0.1942, mean_absolute_error: 0.1658, mean_prior_absolute_error: 0.18, weighted_recall: 0.2031, number_of_instances: 10992, predictive_accuracy: 0.2031, prior_entropy: 3.3208, relative_absolute_error: 0.9215, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.288, root_relative_squared_error: 0.9602, unweighted_recall: 0.1952,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set credit-g

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6506, kb_relative_information_score: 0.061, mean_absolute_error: 0.3766, mean_prior_absolute_error: 0.4202, weighted_recall: 0.7, number_of_instances: 1000, predictive_accuracy: 0.7, prior_entropy: 0.8813, relative_absolute_error: 0.8963, root_mean_prior_squared_error: 0.4583, root_mean_squared_error: 0.4341, root_relative_squared_error: 0.9472, unweighted_recall: 0.5,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set credit-approval

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8407, f_measure: 0.8554, kappa: 0.7116, kb_relative_information_score: 0.5565, mean_absolute_error: 0.2379, mean_prior_absolute_error: 0.494, weighted_recall: 0.8551, number_of_instances: 690, precision: 0.8663, predictive_accuracy: 0.8551, prior_entropy: 0.9912, relative_absolute_error: 0.4815, root_mean_prior_squared_error: 0.497, root_mean_squared_error: 0.3452, root_relative_squared_error: 0.6946, unweighted_recall: 0.862,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set optdigits

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5909, kappa: 0.104, kb_relative_information_score: 0.1232, mean_absolute_error: 0.1679, mean_prior_absolute_error: 0.18, weighted_recall: 0.1947, number_of_instances: 5620, predictive_accuracy: 0.1947, prior_entropy: 3.3218, relative_absolute_error: 0.9327, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2898, root_relative_squared_error: 0.9658, unweighted_recall: 0.1944,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set cmc

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5495, kb_relative_information_score: 0.0624, mean_absolute_error: 0.4091, mean_prior_absolute_error: 0.4308, weighted_recall: 0.427, number_of_instances: 1473, predictive_accuracy: 0.427, prior_entropy: 1.539, relative_absolute_error: 0.9496, root_mean_prior_squared_error: 0.4641, root_mean_squared_error: 0.4523, root_relative_squared_error: 0.9745, unweighted_recall: 0.3333,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set mfeat-zernike

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5807, kappa: 0.095, kb_relative_information_score: 0.1171, mean_absolute_error: 0.1645, mean_prior_absolute_error: 0.18, weighted_recall: 0.1855, number_of_instances: 2000, predictive_accuracy: 0.1855, prior_entropy: 3.3219, relative_absolute_error: 0.914, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2871, root_relative_squared_error: 0.9568, unweighted_recall: 0.1855,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set mfeat-morphological

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.758, kappa: 0.1106, kb_relative_information_score: 0.2861, mean_absolute_error: 0.1606, mean_prior_absolute_error: 0.18, weighted_recall: 0.1995, number_of_instances: 2000, predictive_accuracy: 0.1995, prior_entropy: 3.3219, relative_absolute_error: 0.8923, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2834, root_relative_squared_error: 0.9446, unweighted_recall: 0.1995,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set mfeat-karhunen

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6396, kappa: 0.0972, kb_relative_information_score: 0.1665, mean_absolute_error: 0.1671, mean_prior_absolute_error: 0.18, weighted_recall: 0.1875, number_of_instances: 2000, predictive_accuracy: 0.1875, prior_entropy: 3.3219, relative_absolute_error: 0.9285, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2892, root_relative_squared_error: 0.964, unweighted_recall: 0.1875,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set breast-w

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9246, f_measure: 0.9143, kappa: 0.8108, kb_relative_information_score: 0.7085, mean_absolute_error: 0.1431, mean_prior_absolute_error: 0.4519, weighted_recall: 0.9142, number_of_instances: 699, precision: 0.9146, predictive_accuracy: 0.9142, prior_entropy: 0.9293, relative_absolute_error: 0.3166, root_mean_prior_squared_error: 0.4753, root_mean_squared_error: 0.2756, root_relative_squared_error: 0.5798, unweighted_recall: 0.907,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set mfeat-fourier

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5831, kappa: 0.1011, kb_relative_information_score: 0.1224, mean_absolute_error: 0.1642, mean_prior_absolute_error: 0.18, weighted_recall: 0.191, number_of_instances: 2000, predictive_accuracy: 0.191, prior_entropy: 3.3219, relative_absolute_error: 0.9125, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2867, root_relative_squared_error: 0.9558, unweighted_recall: 0.191,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set mfeat-factors

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5848, kappa: 0.1, kb_relative_information_score: 0.1215, mean_absolute_error: 0.1644, mean_prior_absolute_error: 0.18, weighted_recall: 0.19, number_of_instances: 2000, predictive_accuracy: 0.19, prior_entropy: 3.3219, relative_absolute_error: 0.9133, root_mean_prior_squared_error: 0.3, root_mean_squared_error: 0.2868, root_relative_squared_error: 0.9559, unweighted_recall: 0.19,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set balance-scale

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6114, kappa: 0.2285, kb_relative_information_score: 0.1077, mean_absolute_error: 0.349, mean_prior_absolute_error: 0.3798, weighted_recall: 0.584, number_of_instances: 625, predictive_accuracy: 0.584, prior_entropy: 1.3181, relative_absolute_error: 0.919, root_mean_prior_squared_error: 0.4356, root_mean_squared_error: 0.4259, root_relative_squared_error: 0.9778, unweighted_recall: 0.4225,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set letter

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5495, kappa: 0.0326, kb_relative_information_score: 0.0575, mean_absolute_error: 0.0723, mean_prior_absolute_error: 0.074, weighted_recall: 0.0718, number_of_instances: 20000, predictive_accuracy: 0.0718, prior_entropy: 4.6998, relative_absolute_error: 0.9776, root_mean_prior_squared_error: 0.1923, root_mean_squared_error: 0.1901, root_relative_squared_error: 0.9887, unweighted_recall: 0.0688,

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(colu ... ses.DecisionTreeClassifier)(9) on task Supervised Classification on data set kr-vs-kp

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6664, f_measure: 0.6233, kappa: 0.3396, kb_relative_information_score: 0.2118, mean_absolute_error: 0.397, mean_prior_absolute_error: 0.499, weighted_recall: 0.6605, number_of_instances: 3196, precision: 0.8015, predictive_accuracy: 0.6605, prior_entropy: 0.9986, relative_absolute_error: 0.7955, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.4455, root_relative_squared_error: 0.8919, unweighted_recall: 0.675,

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier) (9)

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…

28 runs0 likes0 downloads0 reach0 impact

sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (8)

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…

0 runs0 likes0 downloads0 reach0 impact

sklearn.impute._base.SimpleImputer (54)

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…

0 runs0 likes0 downloads0 reach0 impact

sklearn.tree._classes.DecisionTreeClassifier (45)

A decision tree classifier.

0 runs0 likes0 downloads0 reach0 impact

Jano P 10 months ago
ran flow sklearn.pipeline.Pipeline(oneh ... onlinearLogisticRegressor))(1) on task Supervised Classification on data set kr-vs-kp

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9823, f_measure: 0.9418, kappa: 0.8833, kb_relative_information_score: 0.8214, mean_absolute_error: 0.0974, mean_prior_absolute_error: 0.499, weighted_recall: 0.9418, number_of_instances: 3196, precision: 0.9419, predictive_accuracy: 0.9418, prior_entropy: 0.9986, relative_absolute_error: 0.1953, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.2151, root_relative_squared_error: 0.4307, unweighted_recall: 0.9413,

sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,symbolicclassifier=HROCH.classifier.SymbolicClassifier(estimator=HROCH.classifier.NonlinearLogisticRegressor)) (1)

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…

1 runs0 likes0 downloads0 reach0 impact

sklearn.preprocessing._encoders.OneHotEncoder (50)

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.…

0 runs0 likes0 downloads0 reach0 impact

sklearn.preprocessing._function_transformer.FunctionTransformer (6)

Constructs a transformer from an arbitrary callable. A FunctionTransformer forwards its X (and optionally y) arguments to a user-defined function or function object and returns the result of this…

0 runs0 likes0 downloads0 reach0 impact

Jano P 10 months ago
ran flow HROCH.classifier.SymbolicClass ... NonlinearLogisticRegressor)(1) on task Supervised Classification on data set MagicTelescope

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8531, f_measure: 0.7939, kappa: 0.5378, kb_relative_information_score: 0.4144, mean_absolute_error: 0.276, mean_prior_absolute_error: 0.456, weighted_recall: 0.8052, number_of_instances: 19020, precision: 0.8093, predictive_accuracy: 0.8052, prior_entropy: 0.9355, relative_absolute_error: 0.6054, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.3728, root_relative_squared_error: 0.7807, unweighted_recall: 0.7486,

HROCH.classifier.SymbolicClassifier(estimator=HROCH.classifier.NonlinearLogisticRegressor) (1)

OVR multiclass symbolic classificator

1 runs0 likes0 downloads0 reach0 impact

HROCH.classifier.NonlinearLogisticRegressor (1)

Nonlinear Logistic Regressor

0 runs0 likes0 downloads0 reach0 impact

Spider King 10 months ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(41) on task Supervised Classification on data set MagicTelescope

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9369, f_measure: 0.8792, kappa: 0.732, kb_relative_information_score: 0.5974, mean_absolute_error: 0.1943, mean_prior_absolute_error: 0.456, weighted_recall: 0.8811, number_of_instances: 19020, precision: 0.8808, predictive_accuracy: 0.8811, prior_entropy: 0.9355, relative_absolute_error: 0.4262, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2988, root_relative_squared_error: 0.6259, unweighted_recall: 0.856,

sklearn.ensemble._forest.RandomForestClassifier (41)

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…

1 runs0 likes0 downloads0 reach0 impact

Donald Wynn 10 months ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(34) on task Supervised Classification on data set MagicTelescope

0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9366, f_measure: 0.8785, kappa: 0.7302, kb_relative_information_score: 0.5973, mean_absolute_error: 0.1944, mean_prior_absolute_error: 0.456, weighted_recall: 0.8804, number_of_instances: 19020, precision: 0.8802, predictive_accuracy: 0.8804, prior_entropy: 0.9355, relative_absolute_error: 0.4263, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2989, root_relative_squared_error: 0.6261, unweighted_recall: 0.8549,

Taniya D 10 months ago
ran flow __main__.Net.fb27c784dc291a25(1) on task Supervised Classification on data set Meta_Album_PNU_Micro

0 likes - 0 downloads - 0 reach - No evaluations yet (or not applicable).

__main__.Net.fb27c784dc291a25 (1)

Automatically created pytorch flow.

1 runs0 likes0 downloads0 reach0 impact

torch.nn.modules.conv.Conv2d.8505b12e597bde2f (1)

Automatically created pytorch flow.

0 runs0 likes0 downloads0 reach0 impact

torch.nn.modules.conv.Conv2d.fd0a875611172a2 (1)

Automatically created pytorch flow.

0 runs0 likes0 downloads0 reach0 impact

torch.nn.modules.linear.Linear.792cbd9a707b5cd (1)