Effective evaluation metrics are crucial in assessing the performance of machine learning models. One of such metrics is the F1 score, which is widely used for classification problems, information retrieval, and NLP tasks.
In this blog post, we’ll explore the foundational concepts of the F1 score, discuss its limitations, and look at use cases across diverse domains.
What is the F1 score in machine learning?
The performance of ML algorithms is measured using a set of evaluation metrics, with model accuracy being among the commonly used ones.
Accuracy calculates the number of correct predictions made by a model across the entire dataset, which is valid when the dataset classes are balanced in size. In the past, accuracy was the sole criterion for comparing machine learning models.
But real-world datasets often exhibit heavy class imbalance, rendering the accuracy metric impractical. For instance, in a binary class dataset with 90 samples in class 1 and 10 samples in class 2, a model that consistently predicts “class 1” would still achieve 90% accuracy. But can we consider this model a good predictor?
Today data scientists use the precision measure alongside accuracy. While accuracy assesses the proximity to the actual value of the measurement, precision…
