Study
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…
0 datasets, 0 tasks, 0 flows, 0 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
A small study of algorithms on datasets provided by the students.
0 datasets, 0 tasks, 0 flows, 0 runs
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used. However, this progress is not yet matched by equal progress on automatic…
0 datasets, 0 tasks, 0 flows, 0 runs
This collection of datasets and runs was used in the study included in the dissertation, prepared by Miguel Viana Cachada, for the Master in Data Analytics from _Faculdade de Economia do Porto_…
0 datasets, 0 tasks, 0 flows, 0 runs
Datasets used to evaluate Layered TPOT against 'vanilla' TPOT. Comprises a selection of large datasets, with between 100k and 1m instances each, contains pseudo-synthetic datasets.
0 datasets, 0 tasks, 0 flows, 0 runs
Run experiments on study 14
0 datasets, 0 tasks, 0 flows, 0 runs
A simple study created for a talk at CENISBS
0 datasets, 0 tasks, 0 flows, 0 runs
This study is intented for exploring the platform. Most things will be deleted.
0 datasets, 0 tasks, 0 flows, 0 runs
Here is description in the form of a tutorial: https://medium.com/@alexrachnog/neural-networks-for-algorithmic-trading-multimodal-and-multitask-deep-learning-5498e0098caf; a link to the Github repo is…
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
Identify best ML for predicting the churn
0 datasets, 0 tasks, 0 flows, 0 runs