pydrift 0.1.6

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.

Welcome to pydrift 0.1.6

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.

Install pydrift :v:

pip install pydrift

Structure :triangular_ruler:

This is intended to be user-friendly. pydrift is divided into DataDriftChecker and ModelDriftChecker:

• DataDriftChecker: search for drift in the variables, check if their distributions have changed
• ModelDriftChecker: search for drift in the relationship of the variables with the target, checks that the model behaves the same way for both data sets

Both can use a discriminative model (defined by parent class DriftChecker), where the target would be binary in belonging to one of the two sets, 1 if it is the left one and 0 on the contrary. If the model is not able to differentiate given the two sets, there is no difference!

Usage :book:

You can take a look to the notebooks folder where you can find one example for DataDriftChecker and other one for ModelDriftChecker.

For more info check the docs available here

More demos and code improvements will coming, if you want to contribute you can contact me, in the future I will upload a file to explain how this would work.