Evaluation Metrics for Classification Explained

Evaluation Metrics for Classification Explained

Selecting appropriate evaluation metrics for multiclass and binary classification problems in Python. Evaluation metric refers to a measure that we use to evaluate different models. Choosing an appropriate evaluation metric is a decision problem that requires a thorough understanding of the goal of a project and is a fundamental step before all modeling process that follows. So why is it so important, and how should we choose?

Evaluation metric refers to a measure that we use to evaluate different models. Choosing an appropriate evaluation metric is a decision problem that requires a thorough understanding of the goal of a project and is a fundamental step before all modeling process that follows. So why is it so important, and how should we choose?


Why is it important?

Let’s say you live in a city where it is sunny about 300 days a year. During the other 65 days, it gets very uncomfortable with heavy rain or snow. In this imaginary world, there are only two weather apps. One app predicts the weather correctly 80% of the time, while the other one is right about 70% of the time. Which app would you choose?

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