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Visualizing drifting features

After ranking the univariate results, you know that drift hotel and country features are impacting the model's performance the most. In this exercise, you will look at the drift results and distribution plots of them to determine the root cause of the problem.

The results from the univariate drift calculator are stored in the uv_results variable.

Diese Übung ist Teil des Kurses

Monitoring Machine Learning in Python

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Anleitung zur Übung

  • Set period argument to analysis for drift_results.
  • Pass hotel and country to column_names for drift_results.
  • Set kind argument in .plot() method to "drift".
  • Do the same for distribution_results, except for setting the kind argument in .plot() method to "distribution".

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Filter and create drift plots
drift_results = uv_results.filter(
    period=____,
    column_names=[____, ____]
    ).plot(kind=____)

# Filter and create distribution plots
distribution_results = uv_results.filter(
    period=____,
    column_names=[____, ____]
    ).plot(kind=____)

# Show the plots
drift_results.show()
distribution_results.show()
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