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Modifying the thresholds

In the video, you observed how NannyML calculates threshold values and learned how to customize them to suit your solution.

In this exercise, your task is to define two custom standard deviation and custom thresholds and then apply them to the results obtained from the CBPE algorithm for the US Census dataset.

The reference and analysis sets have been pre-loaded as reference and analysis, along with the nannyml library.

Este exercício faz parte do curso

Monitoring Machine Learning in Python

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Instruções do exercício

  • Import ConstantThreshold, and StandardDeviationThreshold from nannyml.thresholds.
  • Initialize the standard deviation method and set std_lower_multiplier and std_upper_multiplier parameters to 2.
  • Initialize the constant threshold method and set the lower parameter to 0.9 and upper to 0.98.
  • Pass the constant threshold method for the f1 metric and the standard deviation method for accuracy to the CBPE algorithm.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Import custom thresholds
from ____.____ import ____, ____

# Initialize custom thresholds
stdt = ____(____=____, ____=____)
ct = ____(____=____, ____=____)

# Initialize the CBPE algorithm
estimator = nannyml.CBPE(
    problem_type='classification_binary',
    y_pred_proba='predicted_probability',
    y_pred='prediction',
    y_true='employed',
    metrics=['roc_auc', 'accuracy', 'f1'],
    thresholds={____: ____, ____: ____})
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