Visualizing and Logging Metrics
123456789101112131415161718192021import matplotlib.pyplot as plt import numpy as np # Simulate model metric logging over 12 weeks weeks = np.arange(1, 13) accuracy = np.array([0.89, 0.90, 0.91, 0.91, 0.92, 0.91, 0.90, 0.89, 0.87, 0.85, 0.86, 0.86]) precision = np.array([0.88, 0.88, 0.89, 0.90, 0.89, 0.89, 0.88, 0.87, 0.86, 0.84, 0.85, 0.85]) recall = np.array([0.87, 0.88, 0.90, 0.89, 0.91, 0.90, 0.88, 0.86, 0.85, 0.83, 0.84, 0.84]) plt.figure(figsize=(10, 6)) plt.plot(weeks, accuracy, marker='o', label='Accuracy') plt.plot(weeks, precision, marker='s', label='Precision') plt.plot(weeks, recall, marker='^', label='Recall') plt.axhline(0.88, color='red', linestyle='--', label='Alert Threshold') plt.title('Model Metrics Over Time') plt.xlabel('Week') plt.ylabel('Metric Value') plt.ylim(0.8, 1.0) plt.legend() plt.grid(True) plt.show()
When you monitor model metrics such as accuracy, precision, and recall over time, you gain insight into your model's ongoing performance. Consistent values suggest stable behavior, while noticeable drops—especially below a predefined threshold—can signal underlying issues. A sudden decline in accuracy, for instance, may indicate data drift, changes in user behavior, or upstream data quality problems.
To proactively maintain model reliability, you should set up alerts that trigger when metrics fall below critical thresholds. These alerts can be as simple as email notifications or as sophisticated as automated retraining jobs. The key is to respond quickly to performance changes, minimizing any negative impact on users or business outcomes.
Monitoring should include both model and data quality metrics.
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Completion rate improved to 6.25Visualizing and Logging Metrics
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123456789101112131415161718192021import matplotlib.pyplot as plt import numpy as np # Simulate model metric logging over 12 weeks weeks = np.arange(1, 13) accuracy = np.array([0.89, 0.90, 0.91, 0.91, 0.92, 0.91, 0.90, 0.89, 0.87, 0.85, 0.86, 0.86]) precision = np.array([0.88, 0.88, 0.89, 0.90, 0.89, 0.89, 0.88, 0.87, 0.86, 0.84, 0.85, 0.85]) recall = np.array([0.87, 0.88, 0.90, 0.89, 0.91, 0.90, 0.88, 0.86, 0.85, 0.83, 0.84, 0.84]) plt.figure(figsize=(10, 6)) plt.plot(weeks, accuracy, marker='o', label='Accuracy') plt.plot(weeks, precision, marker='s', label='Precision') plt.plot(weeks, recall, marker='^', label='Recall') plt.axhline(0.88, color='red', linestyle='--', label='Alert Threshold') plt.title('Model Metrics Over Time') plt.xlabel('Week') plt.ylabel('Metric Value') plt.ylim(0.8, 1.0) plt.legend() plt.grid(True) plt.show()
When you monitor model metrics such as accuracy, precision, and recall over time, you gain insight into your model's ongoing performance. Consistent values suggest stable behavior, while noticeable drops—especially below a predefined threshold—can signal underlying issues. A sudden decline in accuracy, for instance, may indicate data drift, changes in user behavior, or upstream data quality problems.
To proactively maintain model reliability, you should set up alerts that trigger when metrics fall below critical thresholds. These alerts can be as simple as email notifications or as sophisticated as automated retraining jobs. The key is to respond quickly to performance changes, minimizing any negative impact on users or business outcomes.
Monitoring should include both model and data quality metrics.
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