0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, kb_relative_information_score: 0.0044, mean_absolute_error: 0.3906, mean_prior_absolute_error: 0.3925, weighted_recall: 0.73, number_of_instances: 100, predictive_accuracy: 0.73, prior_entropy: 0.8415, relative_absolute_error: 0.9952, root_mean_prior_squared_error: 0.444, root_mean_squared_error: 0.444, root_relative_squared_error: 1.0001, usercpu_time_millis: 0.092, usercpu_time_millis_testing: 0.066, usercpu_time_millis_training: 0.026, unweighted_recall: 0.5,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7401, f_measure: 0.741, kappa: 0.4814, kb_relative_information_score: 0.2988, mean_absolute_error: 0.3691, mean_prior_absolute_error: 0.4981, weighted_recall: 0.7416, number_of_instances: 89, precision: 0.7421, predictive_accuracy: 0.7416, prior_entropy: 1.0034, relative_absolute_error: 0.741, root_mean_prior_squared_error: 0.5012, root_mean_squared_error: 0.4411, root_relative_squared_error: 0.88, usercpu_time_millis: 0.016, usercpu_time_millis_testing: 0.004, usercpu_time_millis_training: 0.012, unweighted_recall: 0.7401,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9993, f_measure: 0.9928, kappa: 0.9856, kb_relative_information_score: 0.9406, mean_absolute_error: 0.0355, mean_prior_absolute_error: 0.499, weighted_recall: 0.9928, number_of_instances: 3196, precision: 0.9928, predictive_accuracy: 0.9928, prior_entropy: 0.9986, relative_absolute_error: 0.0711, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.0955, root_relative_squared_error: 0.1912, usercpu_time_millis: 11.621, usercpu_time_millis_testing: 0.667, usercpu_time_millis_training: 10.954, unweighted_recall: 0.9928,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.499, kb_relative_information_score: 0, mean_absolute_error: 0.499, mean_prior_absolute_error: 0.499, weighted_recall: 0.5222, number_of_instances: 3196, predictive_accuracy: 0.5222, prior_entropy: 0.9986, relative_absolute_error: 1, root_mean_prior_squared_error: 0.4995, root_mean_squared_error: 0.4995, root_relative_squared_error: 1, usercpu_time_millis: 1.104, usercpu_time_millis_testing: 0.572, usercpu_time_millis_training: 0.532, unweighted_recall: 0.5,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.5, kb_relative_information_score: 0.0044, mean_absolute_error: 0.3906, mean_prior_absolute_error: 0.3925, weighted_recall: 0.73, number_of_instances: 100, predictive_accuracy: 0.73, prior_entropy: 0.8415, relative_absolute_error: 0.9952, root_mean_prior_squared_error: 0.444, root_mean_squared_error: 0.444, root_relative_squared_error: 1.0001, usercpu_time_millis: 0.013, usercpu_time_millis_testing: 0.003, usercpu_time_millis_training: 0.01, unweighted_recall: 0.5,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8504, f_measure: 0.912, kappa: 0.0804, kb_relative_information_score: -0.0913, mean_absolute_error: 0.1067, mean_prior_absolute_error: 0.1186, weighted_recall: 0.9369, number_of_instances: 2534, precision: 0.9119, predictive_accuracy: 0.9369, prior_entropy: 0.3398, relative_absolute_error: 0.9, root_mean_prior_squared_error: 0.2432, root_mean_squared_error: 0.232, root_relative_squared_error: 0.954, usercpu_time_millis: 31.937, usercpu_time_millis_testing: 0.556, usercpu_time_millis_training: 31.381, unweighted_recall: 0.5233,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8504, f_measure: 0.912, kappa: 0.0804, kb_relative_information_score: -0.0913, mean_absolute_error: 0.1067, mean_prior_absolute_error: 0.1186, weighted_recall: 0.9369, number_of_instances: 2534, precision: 0.9119, predictive_accuracy: 0.9369, prior_entropy: 0.3398, relative_absolute_error: 0.9, root_mean_prior_squared_error: 0.2432, root_mean_squared_error: 0.232, root_relative_squared_error: 0.954, usercpu_time_millis: 31.937, usercpu_time_millis_testing: 0.556, usercpu_time_millis_training: 31.381, unweighted_recall: 0.5233,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9831, f_measure: 0.988, kappa: 0.9758, kb_relative_information_score: 0.9587, mean_absolute_error: 0.0234, mean_prior_absolute_error: 0.4938, weighted_recall: 0.988, number_of_instances: 500, precision: 0.9883, predictive_accuracy: 0.988, prior_entropy: 0.9909, relative_absolute_error: 0.0474, root_mean_prior_squared_error: 0.4969, root_mean_squared_error: 0.1085, root_relative_squared_error: 0.2183, usercpu_time_millis: 0.329, usercpu_time_millis_testing: 0.023, usercpu_time_millis_training: 0.306, unweighted_recall: 0.9892,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9831, f_measure: 0.988, kappa: 0.9758, kb_relative_information_score: 0.9587, mean_absolute_error: 0.0234, mean_prior_absolute_error: 0.4938, weighted_recall: 0.988, number_of_instances: 500, precision: 0.9883, predictive_accuracy: 0.988, prior_entropy: 0.9909, relative_absolute_error: 0.0474, root_mean_prior_squared_error: 0.4969, root_mean_squared_error: 0.1085, root_relative_squared_error: 0.2183, usercpu_time_millis: 0.813, usercpu_time_millis_testing: 0.022, usercpu_time_millis_training: 0.791, unweighted_recall: 0.9892,
Learner mlr.classif.rpart.preproc from package(s) rpart.
2 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.6624, f_measure: 0.745, kappa: 0.3683, kb_relative_information_score: 0.4025, mean_absolute_error: 0.234, mean_prior_absolute_error: 0.4202, weighted_recall: 0.766, number_of_instances: 1000, precision: 0.7542, predictive_accuracy: 0.766, prior_entropy: 0.8813, relative_absolute_error: 0.5569, root_mean_prior_squared_error: 0.4583, root_mean_squared_error: 0.4837, root_relative_squared_error: 1.0556, usercpu_time_millis: 15.274, usercpu_time_millis_testing: 0.195, usercpu_time_millis_training: 15.079, unweighted_recall: 0.6624,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9959, f_measure: 0.96, kappa: 0.94, kb_relative_information_score: 0.927, mean_absolute_error: 0.0386, mean_prior_absolute_error: 0.4444, weighted_recall: 0.96, number_of_instances: 150, precision: 0.96, predictive_accuracy: 0.96, prior_entropy: 1.585, relative_absolute_error: 0.0868, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1456, root_relative_squared_error: 0.309, usercpu_time_millis: 0.278, usercpu_time_millis_testing: 0.018, usercpu_time_millis_training: 0.26, unweighted_recall: 0.96,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9959, f_measure: 0.96, kappa: 0.94, kb_relative_information_score: 0.927, mean_absolute_error: 0.0386, mean_prior_absolute_error: 0.4444, weighted_recall: 0.96, number_of_instances: 150, precision: 0.96, predictive_accuracy: 0.96, prior_entropy: 1.585, relative_absolute_error: 0.0868, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1456, root_relative_squared_error: 0.309, usercpu_time_millis: 0.274, usercpu_time_millis_testing: 0.023, usercpu_time_millis_training: 0.251, unweighted_recall: 0.96,
Learner mlr.classif.randomForest from package(s) randomForest.
2 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.97, f_measure: 0.96, kappa: 0.94, kb_relative_information_score: 0.9452, mean_absolute_error: 0.0267, mean_prior_absolute_error: 0.4444, weighted_recall: 0.96, number_of_instances: 150, precision: 0.96, predictive_accuracy: 0.96, prior_entropy: 1.585, relative_absolute_error: 0.06, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1633, root_relative_squared_error: 0.3464, usercpu_time_millis: 0.508, usercpu_time_millis_testing: 0.015, usercpu_time_millis_training: 0.493, unweighted_recall: 0.96,
Learner mlr.classif.randomForest from package(s) randomForest.
2 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.rpart from package(s) rpart.
0 runs0 likes0 downloads0 reach0 impact
TALLO - a global tree allometry and crown architecture database. This is the Tallo dataset described in Jucker et al. (2022) but recreated with Python scripts from Laurens Bliek. The scripts can be…
0 runs0 likes0 downloads0 reach0 impact
307014 instances - 21 features - 0 classes - 0 missing values
We introduce how we configured benchmark datasets to properly evaluate the performance of our proposed method, STCC: Semi-Supervised Learning for Tabular Datasets with Continuous and Categorical…
24 datasets, 24 tasks, 0 flows, 0 runs
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 1.0146, number_of_instances: 303, root_mean_prior_squared_error: 1.2265,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
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…
9 runs0 likes0 downloads0 reach0 impact
Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
0 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
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
A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive…
0 runs0 likes0 downloads0 reach0 impact
this is for test
24 datasets, 24 tasks, 0 flows, 0 runs
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
0 runs0 likes0 downloads0 reach0 impact
uploader_id : 31892 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as…
0 runs0 likes0 downloads0 reach0 impact
1000000 instances - 26 features - 0 classes - 0 missing values
Data reported to the police about the circumstances of personal injury road accidents in Great Britain from 1979, and the maker and model information of vehicles involved in the respective…
0 runs0 likes0 downloads0 reach0 impact
111762 instances - 33 features - 0 classes - 0 missing values
Nomao collects data about places (name, phone, localization...) from many sources. Deduplication consists in detecting what data refer to the same place. Instances in the dataset compare 2 spots.The…
0 runs0 likes0 downloads0 reach0 impact
34465 instances - 119 features - 0 classes - 0 missing values
The QSAR biodegradation dataset was built in the Milano Chemometrics and QSAR Research Group. The research leading to these results has received funding from the European Communitys Seventh Framework…
0 runs0 likes0 downloads0 reach0 impact
1055 instances - 41 features - 2 classes - 0 missing values
A dataset relating characteristics of telephony account features and usage and whether or not the customer churned. Originally used in Discovering Knowledge in Data: An Introduction to Data…
0 runs0 likes0 downloads0 reach0 impact
5000 instances - 21 features - 0 classes - 0 missing values
This dataset is for classification tasks, and has both continuous and categorical variables.
0 runs0 likes0 downloads0 reach0 impact
5032 instances - 46 features - 0 classes - 0 missing values
An international e-commerce company based wants to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.…
0 runs0 likes0 downloads0 reach0 impact
10999 instances - 10 features - 0 classes - 0 missing values
Jarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in…
0 runs0 likes0 downloads0 reach0 impact
7608 instances - 24 features - 0 classes - 0 missing values
Airlines Dataset Inspired in the regression dataset from Elena Ikonomovska. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.
0 runs0 likes0 downloads0 reach0 impact
539383 instances - 8 features - 0 classes - 0 missing values
Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders.Home Credit strives to…
0 runs0 likes0 downloads0 reach0 impact
244280 instances - 70 features - 0 classes - 0 missing values
One of the biggest challenges of an auto dealership purchasing a used car at an auto auction is the risk of that the vehicle might have serious issues that prevent it from being sold to customers. The…
0 runs0 likes0 downloads0 reach0 impact
67212 instances - 31 features - 0 classes - 0 missing values
The dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information…
0 runs0 likes0 downloads0 reach0 impact
101766 instances - 48 features - 3 classes - 192849 missing values
Prediction task is to determine whether a person makes over 50K a year. Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the…
0 runs0 likes0 downloads0 reach0 impact
48842 instances - 15 features - 2 classes - 0 missing values
User profile data for San Francisco OkCupid users published in [Kim, A. Y., & Escobedo-Land, A. (2015). OKCupid data for introductory statistics and data science courses. Journal of Statistics…
0 runs0 likes0 downloads0 reach0 impact
26677 instances - 14 features - 3 classes - 0 missing values
This dataset is for classification tasks, and has both continuous and categorical variables.
0 runs0 likes0 downloads0 reach0 impact
100959 instances - 30 features - 0 classes - 0 missing values
The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was…
0 runs0 likes0 downloads0 reach0 impact
45211 instances - 17 features - 0 classes - 0 missing values
This dataset is for classification tasks, and has both continuous and categorical variables.
0 runs0 likes0 downloads0 reach0 impact
23548 instances - 11 features - 0 classes - 0 missing values
This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect the confidentiality of the data.This dataset is interesting because…
0 runs0 likes0 downloads0 reach0 impact
653 instances - 16 features - 2 classes - 0 missing values
This data set contains details of a bank's customers and the target variable is a binary variable reflecting the fact whether the customer left the bank (closed his account) or he continues to be a…
0 runs0 likes0 downloads0 reach0 impact
10000 instances - 11 features - 0 classes - 0 missing values
This dataset contain attributes of dresses and their recommendations according to their sales. Sales are monitor on the basis of alternate days.The attributes present analyzed are: Recommendation,…
0 runs0 likes0 downloads0 reach0 impact
500 instances - 13 features - 0 classes - 0 missing values
The dataset consists of feature vectors belonging to 12,330 sessions.The dataset was formed so that each sessionwould belong to a different user in a 1-year period to avoidany tendency to a specific…
0 runs0 likes0 downloads0 reach0 impact
12330 instances - 18 features - 2 classes - 0 missing values
Thyroid disease records supplied by the Garavan Institute and J. Ross Quinlan, New South Wales Institute, Syndney, Australia. 1987.
0 runs0 likes0 downloads0 reach0 impact
3103 instances - 23 features - 2 classes - 0 missing values
This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix:Good Bad (predicted) Good 0 1 (actual)Bad 5 0 It is worse to class a…
0 runs0 likes0 downloads0 reach0 impact
1000 instances - 21 features - 2 classes - 0 missing values
This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed…
0 runs0 likes0 downloads0 reach0 impact
2984 instances - 145 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
0 runs0 likes0 downloads0 reach0 impact
1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
0 runs0 likes0 downloads0 reach0 impact
1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
0 runs0 likes0 downloads0 reach0 impact
1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
0 runs0 likes0 downloads0 reach0 impact
1473 instances - 10 features - 0 classes - 0 missing values
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
0 runs0 likes0 downloads0 reach0 impact
1473 instances - 10 features - 0 classes - 0 missing values
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9367, f_measure: 0.8801, kappa: 0.7339, kb_relative_information_score: 0.5973, mean_absolute_error: 0.1943, mean_prior_absolute_error: 0.456, weighted_recall: 0.882, number_of_instances: 19020, precision: 0.8817, predictive_accuracy: 0.882, prior_entropy: 0.9355, relative_absolute_error: 0.4261, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.299, root_relative_squared_error: 0.6263, unweighted_recall: 0.857,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9359, f_measure: 0.8784, kappa: 0.7301, kb_relative_information_score: 0.5971, mean_absolute_error: 0.1943, mean_prior_absolute_error: 0.456, weighted_recall: 0.8803, number_of_instances: 19020, precision: 0.8799, predictive_accuracy: 0.8803, prior_entropy: 0.9355, relative_absolute_error: 0.4261, root_mean_prior_squared_error: 0.4775, root_mean_squared_error: 0.2994, root_relative_squared_error: 0.6271, unweighted_recall: 0.8551,
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…
2 runs0 likes0 downloads0 reach0 impact
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,
0 likes - 0 downloads - 0 reach - mean_prior_absolute_error: 0.473, number_of_instances: 891, root_mean_prior_squared_error: 0.4863,