You are developing a classification model to support predictions for your company's various products. The dataset you were given for model development has class imbalance You need to minimize false po

The correct answer is A. F1 score. F1 score is the harmonic mean of Precision and Recall: F1 = 2 × (Precision × Recall) / (Precision + Recall). Minimizing false positives requires high Precision; minimizing false negatives requires high Recall. F1 balances both simultaneously, making it the right metric when both

Submitted by eva_at· Apr 18, 2026ML model development

Question

You are developing a classification model to support predictions for your company's various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?

Options

AF1 score
BRecall
CAccuracy
DPrecision

How the community answered

(22 responses)

A
77% (17)
B
5% (1)
C
5% (1)
D
14% (3)

Explanation

F1 score is the harmonic mean of Precision and Recall: F1 = 2 × (Precision × Recall) / (Precision + Recall). Minimizing false positives requires high Precision; minimizing false negatives requires high Recall. F1 balances both simultaneously, making it the right metric when both error types matter and the dataset is imbalanced. Accuracy is misleading under class imbalance (predicting the majority class always yields high accuracy). Recall alone ignores false positives. Precision alone ignores false negatives. F1 is the standard choice when neither false positives nor false negatives can be deprioritized.

Topics

#Evaluation Metrics#Class Imbalance#F1 Score#Classification

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