Classification Model Evaluation : Accuracy, Precision, Recall & F1 Score Explained

Usmar Hyder || AI Specialist on Instagram: "F1 Score || Evaluation Metrics . . In machine learning, the F1 score is a metric used to evaluate the performance of a classification model. It is the harmonic mean of two other metrics: Precision and Recall. ​While "Accuracy" tells you how many total predictions were correct, the F1 score is often more useful because it tells you how well the model handles the "Positive" class specifically, especially when your data is imbalanced."