You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company’s logo. In…

Option A is correct because using F-score where recall is weighed more than precision is a suitable

metric for binary classification with imbalanced data. F-score is a harmonic mean of precision and

recall, which are two metrics that measure the accuracy and completeness of the positive

class1. Precision is the fraction of true positives among all predicted positives, while recall is the

fraction of true positives among all actual positives1. When the data is imbalanced, the positive class

is the minority class, which is usually the class of interest. For example, in this case, the positive class

is the images that contain the company’s logo, which are rare but important to detect. By weighing

recall more than precision, we can emphasize the importance of finding all the positive examples,

even if some false positives are included2.

Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary

classification with imbalanced data. RMSE is a metric that measures the average magnitude of the

errors between the predicted and actual values3. RMSE is suitable for regression problems, where

the target variable is continuous, not for classification problems, where the target variable is

discrete4.

Option C is incorrect because using F1 score is not the best metric for binary classification with

imbalanced data. F1 score is a special case of F-score where precision and recall are equally

weighted1. F1 score is suitable for balanced data, where the positive and negative classes are equally

important and frequent5. However, for imbalanced data, the positive class is more important and

less frequent than the negative class, so F1 score may not reflect the performance of the model

well2.

Option D is incorrect because using F-score where precision is weighed more than recall is not a good

metric for binary classification with imbalanced data. By weighing precision more than recall, we can

emphasize the importance of minimizing the false positives, even if some true positives are

missed2. However, for imbalanced data, the true positives are more important and less frequent

than the false positives, so this metric may not reflect the performance of the model well2.

Reference:

Precision, recall, and F-measure

F-score for imbalanced data

RMSE

Regression vs classification

F1 score

[Imbalanced classification]

[Binary classification]