The methods for evaluating the performance of a binary classification model from the options provided are:

1. Accuracy: This is the ratio of the total number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class.

2. F1 Score: This is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. It is suitable for uneven class distribution problems.

The other two options, mean squared error and r-squared, are typically used for evaluating regression models, not classification models.

Question

The methods for evaluating the performance of a binary classification model from the options provided are:

1. Accuracy: This is the ratio of the total number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class.

2. F1 Score: This is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. It is suitable for uneven class distribution problems.

The other two options, mean squared error and r-squared, are typically used for evaluating regression models, not classification models.

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The methods for evaluating the performance of a binary classification model from the options provided are:

1. Accuracy: This is the ratio of the total number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class.

2. F1 Score: This is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. It is suitable for uneven class distribution problems.

The other two options, mean squared error and r-squared, are typically used for evaluating regression models, not classification models.

Which of the following is a method for evaluating the performance of a binary classification model?Review LaterAccuracymean squared errorf1 scorer-sqaured

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Which of the following is a method for evaluating the performance of a binary classification model?

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