Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Towards Cognitive Automation of Data Science
Authors: Alain Biem, Maria Butrico, Mark Feblowitz, Tim Klinger, Yuri Malitsky, Kenney Ng, Adam Perer, Chandra Reddy, Anton Riabov, Horst Samulowitz, Daby Sow, Gerald Tesauro, Deepak Turaga
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Data from the submitted dataset is then fed into these analytic algorithms, models are constructed, and evaluated to determine the performance of each flow. These results are fed back to the learning controller strategy in Step 5 which then decides which of these flows to continue deploying or to replace by novel flows. |
| Researcher Affiliation | Industry | Alain Biem, Maria A. Butrico, Mark D. Feblowitz, Tim Klinger, Yuri Malitsky, Kenney Ng, Adam Perer, Chandra Reddy, Anton V. Riabov, Horst Samulowitz, Daby Sow, Gerald Tesauro, Deepak Turaga EMAIL IBM Research, Yorktown Heights, NY 10598, USA |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not state that source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using a 'data set' but does not name any specific public datasets or provide concrete access information (link, DOI, formal citation) for any dataset used. |
| Dataset Splits | No | The paper mentions using 'subsets of the data' and 'allocates samples of the data' for iterative performance estimation, but does not provide specific dataset split information (e.g., percentages for train/validation/test splits, or cross-validation details). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and platforms like 'R, Weka, SPSS', 'MARIO system', 'SOFIA', 'Cascade', and 'INFUSE', but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper mentions 'configuration' and 'hyper-parameter exploration strategies' but does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |