people of a village are totally dependent on the farmers for their daily food items. farmers grow new seeds by checking the weather conditions every year. an ai model is being deployed in the village which predicts the chances of heavy rain to alert farmers which helps them in doing the farming at the right time. which evaluation parameter out of precision, recall and f1 score is best to evaluate the performance of this ai model? explain.
Which evaluation parameter out of precision, recall, and F1 score is best to evaluate the performance of this AI model in the village? Explain.
Answer:
In the context of the AI model deployed in the village to predict heavy rain and help farmers with their farming activities, the most suitable evaluation parameter to assess the model’s performance would be the recall. Here’s why recall is the best choice in this scenario:
Recall is the ratio of the true positives to the sum of true positives and false negatives. In this case, high recall indicates the model’s ability to correctly identify the actual occurrences of heavy rain. For the farmers in the village, it is crucial to receive accurate alerts about heavy rain conditions.
In the scenario described, predicting heavy rain is crucial for the farmers to plan their farming activities effectively. A high recall value would ensure that the AI model is successfully capturing a high percentage of the actual heavy rain occurrences. This means fewer instances of heavy rain catching farmers off guard and potentially damaging crops.
While precision and F1 score are also important metrics, in this specific case, the consequences of a false negative (failing to predict heavy rain when it actually happens) are more severe than a false positive (predicting heavy rain when it doesn’t occur). Thus, maximizing recall would be the priority to ensure that farmers receive timely and accurate alerts about heavy rain events, enabling them to make informed decisions regarding their farming activities.