Python package pydrift
How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to predict. `pydrift` tries to facilitate this task to the data scientist, performing this kind of checks and somehow measuring that degradation.
Summary
| Count | 15 occurrences |
|---|---|
| State | Dead |
| Last occurred | |
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| Trend | Increasing |
| In degree | 18 |
| Out degree | 0 |
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