metrics

Metrics

Metrics

Method	Decription
confusion_matrix	Visualize classifier performance  via  a contingency table visualization
plot	Visualize classifier performance via ROC/PR values over a spread of probability threasholds
pstat	Report the execution summary of a given snippet of code using the cProfile run method

from intel_ai_safety.explainer import metrics

Several base metrics are provided for ML/DL classification models. These metrics cover model execution and performance and orient the data scientist to where there is potential for classification bias.

Algorithms

Provided with a classfication model's predictions and their corresponding ground truths, staple performance metrics can be calculated to determine prediction behaviors in the real world. These functions leverage scikit-learn and plotly (eventually) to calculate and visualize said metrics, respectively.

Environment

• Jupyter Notebooks

Metrics

• Performance metrics
  • Confusion Matrix
  • Performance Plots
• Execution metrics
  • Python profiler

Toolkits

• Scikit-learn
• Plotly
• Python Profilers

References

Scikit-learn
Plotly
Python Profiler