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Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.

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randleon/Understanding-Classification-Model-Performance-Metrics-On-Diabetes-Dataset

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Understanding-Classification-Model-Performance-Metrics-On-Diabetes-Dataset

Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions and then compare the results to those of pre-built Python functions that automatically calculate those same metrics.

Graphical output via Python code was used to evaluate the performance of classification models.

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Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.

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