Скачать или смотреть F1 score | Machine learning | Classification Metrics

In the world of machine learning, the F1 score is a commonly used metric to evaluate the performance of a classification model. The F1 score is a measure of a model's accuracy, which takes into account both precision and recall.

Precision is a metric that measures how many of the positive predictions made by the model were actually correct. Recall, on the other hand, measures how many of the actual positive cases were correctly identified by the model.

The F1 score is a balance between these two metrics, and it is calculated as the harmonic mean of precision and recall. A high F1 score indicates a good balance between precision and recall, which means that the model is performing well in both aspects.

In this video, we will discuss the F1 score in detail and explain how it can be used to evaluate the performance of a classification model. We will also cover some of the limitations of the F1 score and how it can be used in combination with other metrics to get a better understanding of a model's performance.


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