Study
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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_…
37 datasets, 37 tasks, 0 flows, 13616 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.
18 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, 1 flows, 60 runs
This study is intented for exploring the platform. Most things will be deleted.
0 datasets, 0 tasks, 0 flows, 0 runs
IDEK, JUST A TEST
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…
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test
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test
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
myemu
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myemu
0 datasets, 0 tasks, 0 flows, 0 runs
Identify best ML for predicting the churn
0 datasets, 0 tasks, 0 flows, 0 runs
prueba
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An study started by Nandana and Mariano in 2016. We started with unsupervised methods, but we could not find good clusters. Later we started with annotated data... and here we are :-)
1 datasets, 2 tasks, 0 flows, 3 runs
This study lists all the experiments described in the paper ...
0 datasets, 1 tasks, 0 flows, 0 runs
ensemble test on diabetes
0 datasets, 0 tasks, 0 flows, 0 runs
No data.
94 datasets, 94 tasks, 6 flows, 2790 runs
test
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test
0 datasets, 0 tasks, 0 flows, 0 runs
ssss
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No data.
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No data.
0 datasets, 0 tasks, 0 flows, 0 runs
testing ball
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test ball
0 datasets, 0 tasks, 0 flows, 0 runs
testing ball
0 datasets, 0 tasks, 0 flows, 0 runs
Containing all datasets, tasks, flows and runs used in the ASLib OpenML Scenario.
441 datasets, 441 tasks, 63 flows, 0 runs
prabhatest
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No data.
0 datasets, 0 tasks, 0 flows, 0 runs
This is just to test the new ctree implementation on various problems to check if there is anything where it fails.
0 datasets, 0 tasks, 6 flows, 1458 runs
Authors: Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren Abstract: Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures…
39 datasets, 39 tasks, 53 flows, 9627 runs
No data.
0 datasets, 0 tasks, 0 flows, 0 runs
All datasets, tasks, flows and setups used for Chapter 6 in the PhD Thesis "Massively Collaborative Machine Learning"
105 datasets, 105 tasks, 27 flows, 0 runs
Study
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this study joins multiple data stream studies
0 datasets, 0 tasks, 0 flows, 0 runs
Iris dataset
0 datasets, 0 tasks, 0 flows, 0 runs
Compare several trees, bagged trees and the random forest.
6 datasets, 6 tasks, 6 flows, 36 runs
Compare several trees, bagged trees and the random forest
6 datasets, 6 tasks, 2 flows, 12 runs
See if it's cool.
0 datasets, 0 tasks, 0 flows, 0 runs
Based on three different tasks we want to compare three versions of ksvm - C-svc C classification - spoc-svc Crammer, Singer native multi-class - kbb-svc Weston, Watkins native multi-class
0 datasets, 0 tasks, 2 flows, 19 runs
Compare random forest with bagged trees
0 datasets, 0 tasks, 5 flows, 35 runs
none
1 datasets, 1 tasks, 2 flows, 15 runs
Paper on OpenML R library. Includes a case study on bagging vs forests
0 datasets, 0 tasks, 0 flows, 80 runs
Study
0 datasets, 0 tasks, 0 flows, 0 runs
An increase in undergraduate registered students in universities largely grown last years. However, the number of graduates remains low. The main cause of this issue is the evasion and / or retention…
0 datasets, 0 tasks, 0 flows, 0 runs
asdf
0 datasets, 0 tasks, 0 flows, 0 runs
Data mining researchers and practitioners often use general rules of thumb or common data mining wisdom, those are so called data-mining myths. Even though, these myths are not always proven or…
394 datasets, 394 tasks, 25 flows, 11780 runs
a test study. has no value!
0 datasets, 0 tasks, 0 flows, 0 runs
numerai
0 datasets, 0 tasks, 0 flows, 0 runs
A subgroup discovery study.
0 datasets, 3879 tasks, 4 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…
60 datasets, 60 tasks, 6 flows, 389 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
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…
100 datasets, 100 tasks, 372 flows, 600 runs
Almost every form of statistical and machine learning method has been applied to learning QSARs at one time or another: linear regression, decision trees, neural networks, nearest-neighbour methods,…
16941 datasets, 0 tasks, 0 flows, 24034 runs
The work will be submitted to ECML-PKDD2016
0 datasets, 0 tasks, 0 flows, 0 runs
Ensembles of classifiers are among the best performing classifiers available in many data mining applications. However, most ensembles developed specifically for the dynamic data stream setting rely…
0 datasets, 62 tasks, 13 flows, 805 runs
Example of collaborative research conducted by means of OpenML NB:
2 datasets, 0 tasks, 0 flows, 0 runs
how should I proceed? [![run at everware](https://img.shields.io/badge/run…
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No data.
0 datasets, 0 tasks, 0 flows, 0 runs
In this study, we investigate and summarize the performance of a wide range of ML algorithms (using its default hyper-parameter values) on a wide range of OpenML classifications tasks. This will yield…
413 datasets, 425 tasks, 104 flows, 17652 runs
To see what a study can do
0 datasets, 0 tasks, 0 flows, 0 runs
This study compares the local and global feature selection strategy on multilabel classification transformation methods
0 datasets, 0 tasks, 0 flows, 0 runs
test
1 datasets, 0 tasks, 0 flows, 0 runs
The task of Quantitative Structure Activity Relationship (QSAR) Learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the…
1086 datasets, 1081 tasks, 1 flows, 0 runs
One of the challenges in Machine Learning to find a classifier and parameter settings that work well on a given dataset. Evaluating all possible combinations typically takes too much time, hence many…
0 datasets, 39 tasks, 53 flows, 9627 runs
We investigate the performance of a wide range of classification algorithms on a wide range of datasets to better understand when they perform well and when they don't. This will yield a meta-dataset…
511 datasets, 514 tasks, 63 flows, 91425 runs