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[ML] Outlier Detection Example
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# advent-outliers | ||
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To run this example, please ensure that you have the following: | ||
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* an elasticsearch cluster (version 7.2+) running on `localhost:9200` | ||
* the dependencies listed in `requirements.txt`. To install these, please run - `pip install -r requirements.txt` |
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Machine Learning/Outlier Detection/Introduction/advent-outliers.ipynb
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Machine Learning/Outlier Detection/Introduction/requirements.txt
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jupyter | ||
numpy | ||
requests | ||
elasticsearch | ||
sklearn |
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# Outlier Detection | ||
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This directory contains examples showcasing Elastic's outlier detection analyses within Machine Learning, which was released in version 7.2. | ||
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* [Introduction](Introduction/) shows you how to think about outliers in a 2-dimensional case. We walk you through building up a dataset with particular features and demonstrates how to create and start an outlier detection analysis, and how to analyze the outlier score and feature influence results. |