Study
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
test creation of a benchmark suite
1 datasets, 1 tasks, 0 flows, 0 runs
test creation of a benchmark suite
1 datasets, 1 tasks, 0 flows, 0 runs
test creation of a benchmark suite
1 datasets, 1 tasks, 0 flows, 0 runs
test creation of a benchmark suite
1 datasets, 1 tasks, 0 flows, 0 runs
test creation of a benchmark suite
1 datasets, 1 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
na
2 datasets, 2 tasks, 2 flows, 2 runs
na
2 datasets, 2 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
HalvingRandomSearchCV sh_defaults on CC18
0 datasets, 0 tasks, 0 flows, 0 runs
SH on first task of CC18
0 datasets, 0 tasks, 0 flows, 0 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
Run results of the ongoing AutoML benchmark, see https://openml.github.io/automlbenchmark/.The benchmark includes both binary and multiclass classification tasks.
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.
0 datasets, 0 tasks, 0 flows, 0 runs
Tasks of the ongoing AutoML benchmark, see https://openml.github.io/automlbenchmark/.The benchmark includes both binary and multiclass classification tasks.
0 datasets, 0 tasks, 0 flows, 0 runs
A short description of the benchmark suite.
0 datasets, 0 tasks, 0 flows, 0 runs
A short description of the benchmark suite.
0 datasets, 0 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…
0 datasets, 0 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
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
aaa
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
first experiment
0 datasets, 0 tasks, 0 flows, 0 runs
Primeiro teste
0 datasets, 0 tasks, 0 flows, 0 runs
testmm1
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
ZxZxZx
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
No data.
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
a
a
0 datasets, 0 tasks, 0 flows, 0 runs
AFH
0 datasets, 0 tasks, 0 flows, 0 runs
text
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
Test
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
1
0 datasets, 0 tasks, 0 flows, 0 runs
The library contains different multi-class datasets.
0 datasets, 0 tasks, 0 flows, 0 runs
just messing around
0 datasets, 0 tasks, 0 flows, 0 runs
Workflow recomendation experiment using runs considered "human-made"
0 datasets, 0 tasks, 0 flows, 0 runs