Interacting with results
In this exercise, you will filter, plot, and convert to the DataFrame the CBPE results obtained for the US Consensus dataset from the previous example. The display method here is used to show the plots and DataFrames that are called in the middle of the code.
The results from the CBPE estimator are preloaded in the estimated_results variable.
This exercise is part of the course
Monitoring Machine Learning in Python
Exercise instructions
- Interact with the estimated results based on the comments above each code snippet.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Filter estimated results for the roc_auc metric and convert them to a dataframe
display(estimated_results.____(____=[____]).____())
# Filter estimated results for the reference period and convert them to a dataframe
display(estimated_results.____(____=____).____())
# Filter the estimated results for the accuracy metric
display(estimated_results.filter(____=____).plot().show())
# Filter the estimated results for the analysis period, as well as for accuracy and roc_auc metrics
display(estimated_results.filter(____=____, ____=[____, ____]).plot().show())