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SIIM ISIC Ensemble Baseline Predictions
Patient Health Records & Digital Health
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About
This collection presents several baseline models designed for ensemble and stacking methods, specifically developed for the SIIM ISIC challenge related to cancer classification. The data provides crucial prediction targets and image identifiers necessary for researchers aiming to improve or replicate complex deep learning strategies. Users should note that employing the
rankdata function is recommended, as the included submission files possess different data distributions. The models themselves are fundamental resources for classification tasks.Columns
- image_name: This identifies the specific image associated with the prediction. It contains 11.0k valid entries, with 10,982 unique values. The most frequent image name encountered is ISIC_0052060.
- target: This represents the prediction score or target label output by the baseline model. The majority of scores fall between 0.00 and 0.09 (10,407 counts). Statistical measures show a Mean of 0.03 and a Standard Deviation of 0.08.
Distribution
The data is structured for ease of use, exemplified by a file such as EFFNET_0952.csv, which is approximately 307.51 kB in size. The dataset contains 2 columns and 11.0k valid records. It is usually supplied in CSV format, ready for analysis and integration into machine learning pipelines.
Usage
This data is ideally suited for tasks involving Deep Learning and Ensembling techniques. It serves as an excellent starting point for researchers focused on improving model performance through stacking multiple submission files. Primary use cases include cancer image classification, testing advanced machine learning model fusion techniques, and benchmarking results against established baseline submissions from the SIIM ISIC challenge.
Coverage
The scope of this dataset is focused on the domain of medical image analysis, specifically concerning cancer classification models. The data pertains to the outcomes of models submitted to the SIIM ISIC challenge. Specific geographical or time range limits are not detailed, but the focus is strictly on the provided image names and derived targets.
License
CC0: Public Domain
Who Can Use It
- Machine Learning Researchers: For developing and evaluating novel ensemble and stacking methodologies.
- Data Scientists: Interested in applying advanced Deep Learning techniques to medical image data.
- Academics: Studying classification algorithms and performance metrics related to large-scale medical challenges.
Dataset Name Suggestions
- SIIM ISIC Ensemble Baseline Predictions
- Cancer Classification Model Outputs
- ISIC Deep Learning Submission Scores
- Medical Image Ensembling Data
Attributes
Original Data Source: SIIM ISIC Ensemble Baseline Predictions
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