Study
Suite containing the datasets used in the "classification on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has…
7 datasets, 7 tasks, 0 flows, 0 runs
this is a collection
1 datasets, 1 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
1 datasets, 1 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "classification on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has…
7 datasets, 7 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "regression on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has been…
13 datasets, 13 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "classification on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has been transformed…
15 datasets, 15 tasks, 0 flows, 0 runs
Suite containing the datasets used in the "regression on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has been transformed as…
20 datasets, 20 tasks, 0 flows, 0 runs
An study reporting results of a decision tree with different splitter.
2 datasets, 2 tasks, 1 flows, 2 runs
An example study reporting results of a decision stump.
2 datasets, 2 tasks, 1 flows, 2 runs
An example study reporting results of a decision stump.
1 datasets, 1 tasks, 1 flows, 1 runs
A complimentary set of tasks to the AutoML benchmark that can be used as a training set for meta-learning as suggested by Feurer et al. in the paper "Auto-Sklearn 2.0: Hands-free AutoML via…
208 datasets, 208 tasks, 0 flows, 0 runs
This collection is a *Curated set of Tabular Regression problems* containing datasets from a multitude of domains. All datasets from this benchmark suite satisfy the following conditions to the best…
0 datasets, 0 tasks, 0 flows, 0 runs
After exploring dierent subsets, the subset consisting of 50% of the total datasets.
29 datasets, 29 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
250 datasets, 250 tasks, 0 flows, 0 runs
illustrating how to create a benchmark suite
250 datasets, 250 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
20 datasets, 20 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
20 datasets, 20 tasks, 0 flows, 0 runs
Test suite for the Python tutorial on benchmark suites
20 datasets, 20 tasks, 0 flows, 0 runs
Description
2 datasets, 2 tasks, 1 flows, 2 runs
Description
39 datasets, 39 tasks, 1 flows, 39 runs
illustrating how to create a benchmark suite
251 datasets, 251 tasks, 0 flows, 0 runs
case_Classifier for the PHM
1 datasets, 1 tasks, 2 flows, 2 runs
case_Regression for the PHM
1 datasets, 1 tasks, 3 flows, 3 runs
Collection of all classification tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
66 datasets, 66 tasks, 0 flows, 0 runs
Collection of new classification tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
29 datasets, 29 tasks, 0 flows, 0 runs
Collection of regression tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
33 datasets, 33 tasks, 0 flows, 0 runs
15% More difficult and discriminative Percentage of instances with high Discrimination parameter values Dataset :: Percentage of instances vowel :: 92% breast-w :: 92% monks-problems-1 :: 89%…
8 datasets, 8 tasks, 0 flows, 0 runs
10% More difficult and discriminative of the TesteCC18 study Porcentagem de instancias com valores altos do parametro Discriminacao Dataset :: Percentual de instancias banknote-authentication :: 100%…
12 datasets, 12 tasks, 0 flows, 0 runs
Testing how to create a benchmark suite
60 datasets, 60 tasks, 0 flows, 0 runs
Benchmark suite for fair machine learning.
0 datasets, 0 tasks, 0 flows, 0 runs
A benchmark suite to investigate how Deep Learning scales with dataset size. Building upon the prior work from https://openml.github.io/automlbenchmark/
62 datasets, 62 tasks, 0 flows, 0 runs
IRT for regression tasks/datasets
0 datasets, 0 tasks, 0 flows, 0 runs
IRT for classificaion tasks/datasets
0 datasets, 0 tasks, 0 flows, 0 runs
SH on first task of CC18
2 datasets, 2 tasks, 2 flows, 4 runs
SH vs RS on first tasks of CC18
0 datasets, 0 tasks, 0 flows, 0 runs
SH on first task of CC18
0 datasets, 0 tasks, 0 flows, 0 runs
Results from the original AutoML benchmark paper presented in “An Open Source AutoML Benchmark” by Gijsbers et al. at the AutoML workshop at ICML 2019. It contains the results of running several…
19 datasets, 19 tasks, 6 flows, 117 runs
Subset of the OpenML100, with datasets that are friedly towards scikit-learn algorithms (no Imputation or One-hot-encoding necessary)
0 datasets, 0 tasks, 0 flows, 0 runs
Contains currency trading tasks, for various valuta pairs.
192 datasets, 192 tasks, 0 flows, 0 runs
The original set of tasks for the AutoML benchmark presented in “An Open Source AutoML Benchmark” by Gijsbers et al. at the AutoML workshop at ICML 2019. The set of tasks aims to provide a…
39 datasets, 39 tasks, 0 flows, 0 runs
Comparison of linear and non-linear models. [Jupyter Notebook](https://github.com/janvanrijn/linear-vs-non-linear/blob/master/notebook/Linear-vs-Non-Linear.ipynb)
0 datasets, 0 tasks, 0 flows, 0 runs
Ensembles of classifiers are among the best performing classifiers available in many data mining applications. Rather than training one classifier, multiple classifiers are trained, and their…
0 datasets, 0 tasks, 0 flows, 0 runs
Feature selection can be of value to classification for a variety of reasons. Real world data sets can be rife with irrelevant features, especially if the data was not gather specifically for the…
394 datasets, 394 tasks, 24 flows, 9454 runs
We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java…
72 datasets, 72 tasks, 0 flows, 0 runs
Benchmarking in Machine Learning is often much more difficult than it seems, and hard to reproduce. This study is a new approach to do a collaborative, in-depth benchmarking of algorithms, and allows…
0 datasets, 0 tasks, 0 flows, 0 runs
Multi-class Classification Study
0 datasets, 0 tasks, 0 flows, 0 runs
This book intends to provide an overview of Machine Learning and its algorithms & models with help of R software. Machine learning forms the basis for Artificial Intelligence which will play a crucial…
0 datasets, 0 tasks, 0 flows, 0 runs
Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep…
0 datasets, 0 tasks, 0 flows, 0 runs
jhuilj;kl
0 datasets, 0 tasks, 0 flows, 0 runs
Prediction of House price
0 datasets, 0 tasks, 0 flows, 0 runs
Ggg
0 datasets, 0 tasks, 0 flows, 0 runs
na
0 datasets, 0 tasks, 0 flows, 0 runs
Hs
0 datasets, 0 tasks, 0 flows, 0 runs
qwerqwe
0 datasets, 0 tasks, 0 flows, 0 runs
Test study for arusov
0 datasets, 0 tasks, 0 flows, 0 runs
hahaha
0 datasets, 0 tasks, 0 flows, 0 runs
Admissions123
0 datasets, 0 tasks, 0 flows, 0 runs
A study of imbalanced classification data benchmarks from KEEL.
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
Learning Tensorflow
0 datasets, 0 tasks, 0 flows, 0 runs
Random
0 datasets, 0 tasks, 0 flows, 0 runs
Android phone scenarios
0 datasets, 0 tasks, 0 flows, 0 runs
Detect breast cancer using various methods
0 datasets, 0 tasks, 0 flows, 0 runs
This is a Machine Learning starter project, we will grab data through online resources and then will perform different algorithms on data.
0 datasets, 0 tasks, 0 flows, 0 runs
Primeiro teste
0 datasets, 0 tasks, 0 flows, 0 runs
xray
0 datasets, 0 tasks, 0 flows, 0 runs
a
0 datasets, 0 tasks, 0 flows, 0 runs
Tweets Demo
0 datasets, 0 tasks, 0 flows, 0 runs
machine language
0 datasets, 0 tasks, 0 flows, 0 runs
A classifier for identifying inconcise mappings in DBpedia based on a set of features defined in the following paper. Rico, Mariano, Mihindukulasooriya, Nandana, Kontokostas, Dimitris, Paulheim,…
0 datasets, 0 tasks, 0 flows, 0 runs
Classification Datasets that are not too large (less than 40k rows) with at least one categorical column
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
A list of the datasets used in the paper Annotative Expert For Hyperparameter Selection, as part of the AutoML workshop at ICML 2018
0 datasets, 0 tasks, 0 flows, 0 runs
trial for learning
0 datasets, 0 tasks, 0 flows, 0 runs
Test on wdbc dataset
0 datasets, 0 tasks, 0 flows, 0 runs
classify
0 datasets, 0 tasks, 0 flows, 0 runs
stanford stuff
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
Selected regression problems for aggregate model analysis
30 datasets, 1 tasks, 0 flows, 0 runs
AFH
0 datasets, 0 tasks, 0 flows, 0 runs
Recoil estimation from drifting position data.
0 datasets, 0 tasks, 0 flows, 0 runs
Show how one-hot-encoding impacts the performance of decision trees. See also https://roamanalytics.com/2016/10/28/are-categorical-variables-getting-lost-in-your-random-forests/
0 datasets, 0 tasks, 0 flows, 0 runs
My first test on the platform
0 datasets, 0 tasks, 0 flows, 0 runs
Dependency parser for news data
0 datasets, 0 tasks, 0 flows, 0 runs
We want to predict the type of a DBpedia resource from its structure in the Knowledge graph. our preliminary study concludes that we can achieve it with accuracy above 90%. Paper submitted to ICWE…
0 datasets, 0 tasks, 0 flows, 0 runs
Studying Weather with machine learning
0 datasets, 0 tasks, 0 flows, 0 runs
Runs made for constructing a meta-dataset in a study on the effects of sparsity on the meta-level.
0 datasets, 0 tasks, 0 flows, 0 runs
Test Of Random
0 datasets, 0 tasks, 0 flows, 0 runs
Benchmark study, using 73 datasets from OpenML-CC18, on the importance of hyperparameter tuning: which parameters are important to tune and which might be set to a default value instead? For each…
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
First analysis of CML survey results from over a year
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
[Sport Data Valley](https://www.sportinnovator.nl/sport-data-valley) is a Dutch initiative to collect, share and analyse datasets on sports and exercise.…
0 datasets, 0 tasks, 0 flows, 0 runs
Data prefetching is a standard technique used to accelerate the access to data stores. In the context of SPARQL endpoints, previous approaches have been based on two main techniques: (1) query…
3 datasets, 3 tasks, 0 flows, 5 runs
Paper submitted to ESWC 2018
0 datasets, 0 tasks, 0 flows, 0 runs
Datasets
0 datasets, 0 tasks, 0 flows, 0 runs
project
0 datasets, 0 tasks, 0 flows, 0 runs
Classifiers in R
0 datasets, 0 tasks, 0 flows, 0 runs
1
0 datasets, 0 tasks, 0 flows, 0 runs