Precision vs Recall Explained Simply | Classification Metrics

Precision and Recall are two important classification metrics, and they are often confused. In this video, we explain Precision vs Recall in a simple way: what Precision means what Recall means how they relate to True Positives, False Positives, and False Negatives why Precision means fewer false alarms why Recall means fewer missed cases when each metric matters more Precision asks: “Of the positives we predicted, how many were truly positive?” Recall asks: “Of all the real positives, how many did we catch?” In the next video, we’ll continue with F1 Score. Like this video, follow for more, and subscribe so you don’t miss the rest of the Machine Learning series. #MachineLearning #Precision #Recall #Classification #ConfusionMatrix #DataScience #MLBasics #ArtificialIntelligence #TechWithAdyn