OpenML
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[Sport Data Valley](https://www.sportinnovator.nl/sport-data-valley) is a Dutch initiative to collect, share and analyse datasets on sports and exercise.…
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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
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Datasets
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project
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Classifiers in R
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The library contains different multi-class datasets.
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just messing around
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Workflow recomendation experiment using runs considered "human-made"
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A small study of algorithms on datasets provided by the students.
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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…
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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_…
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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.
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Run experiments on study 14
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A simple study created for a talk at CENISBS
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This study is intented for exploring the platform. Most things will be deleted.
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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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Identify best ML for predicting the churn
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This was an study started by Nandana and Mariano in 2016. We started with unsupervised methods, but we could not find good clusters. En 2017 we started with annotated data and here we are. ## Summary…
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This study lists all the experiments described in the paper ...
157 datasets, 0 tasks, 0 flows, 0 runs
ensemble test on diabetes
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Containing all datasets, tasks, flows and runs used in the ASLib OpenML Scenario.
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This is just to test the new ctree implementation on various problems to check if there is anything where it fails.
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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…
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All datasets, tasks, flows and setups used for Chapter 6 in the PhD Thesis "Massively Collaborative Machine Learning"
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this study joins multiple data stream studies
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Iris dataset
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Compare several trees, bagged trees and the random forest.
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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
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Paper on OpenML R library. Includes a case study on bagging vs forests
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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…
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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…
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A subgroup discovery study.
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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,…
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The work will be submitted to ECML-PKDD2016
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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…
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Example of collaborative research conducted by means of OpenML NB:
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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…
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This study compares the local and global feature selection strategy on multilabel classification transformation methods
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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…
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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…
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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…
512 datasets, 514 tasks, 63 flows, 91425 runs