Google Professional Machine Learning Engineer
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As a junior data scientist at a financial services company, you're tasked with developing a logistic regression model to categorize incoming customer text messages into two categories: 'important/urgent' and 'important/not urgent'. The model's performance is critical for timely customer service responses. Given the binary nature of the classification task and the need for probabilistic outcomes between 0 and 1, which loss function is most suitable for evaluating your model's performance? Choose the best option.
As a junior data scientist at a financial services company, you're tasked with developing a logistic regression model to categorize incoming customer text messages into two categories: 'important/urgent' and 'important/not urgent'. The model's performance is critical for timely customer service responses. Given the binary nature of the classification task and the need for probabilistic outcomes between 0 and 1, which loss function is most suitable for evaluating your model's performance? Choose the best option.
Explanation:
Log Loss is the optimal loss function for a logistic regression model in a binary classification task because it effectively penalizes incorrect classifications and is sensitive to the probabilities predicted by the model. This makes it particularly suitable for tasks where the outcome is probabilistic and between 0 and 1. Mean Square Error, while useful for regression tasks, does not perform as well in probabilistic contexts. Mean Absolute Error and Mean Bias Error are less effective for classification tasks as they do not emphasize the magnitude of errors as significantly. Softmax is designed for multi-class classification and is not appropriate for binary classification tasks. Therefore, Log Loss is the most suitable choice for evaluating the model's performance in this scenario.