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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.

Este exercício faz parte do curso

Monitoring Machine Learning in Python

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Instruções do exercício

  • 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".

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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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