Exam Professional Machine Learning Engineer topic 1 question 268 discussion

I go with D. log_classification_metrics currently support confusion matrix and ROC curve. https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#google_cloud_aiplatform_log_classification_metrics Because it is not explicitly mentioned in the docs of log_classification_metrics, I assume F1 Score must be logged with log_metrics. https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#google_cloud_aiplatform_log_metrics (if accuracy and recall are logged in the example, probably F1 is done the same way)

In this example linked below you can see that they use log_classification_metrics and the output contains both F1 score and confusion matrix. Hence I am voting for B. https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Writing_Classification_Performance_Metrics_to_WhyLabs.ipynb

D. Utilize the aiplatform.log_metrics function to log the F1 score, and employ the aiplatform.log_classification_metrics function to log the confusion matrix. Utilize the aiplatform.log_metrics function to log the F1 score, and employ the aiplatform.log_classification_metrics function to log the confusion matrix. This is the correct approach. aiplatform.log_metrics is appropriate for logging general metrics such as the F1 score, and aiplatform.log_classification_metrics is ideal for logging classification-specific metrics like the confusion matrix. Reference: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#google_cloud_aiplatform_log_classification_metrics

I think we can log both metrics together. D is a close second but B seems to be a better answer

d

https://cloud.google.com/vertex-ai/docs/experiments/log-data#classification-metrics log_classification_metrics -> only the confusion matrix, not the F1scores log_metrics -> any number you want -> you can use it to store a F1 scores

aiplatform.log_classification_metrics is specifically designed for logging classification metrics, which includes F1 score and confusion matrix. aiplatform.log_metrics is a more generic function for logging any kind of metric, but it wouldn't capture the rich structure of a confusion matrix. Therefore, using aiplatform.log_classification_metrics allows you to log both F1 score and confusion matrix in a single call, simplifying your code and ensuring proper handling of these classification-specific metrics.

According to docs, log_classification_metrics supports confusion matrix and ROC curve. Not sure if it means that it only supports those... Assuming those are the only ones supported, I would got with D

aiplatform.log_classification_metrics to log metrics relevant to classification tasks, including F1 score and confusion matrix.

The aiplatform.log_classification_metrics function is designed to log classification metrics, including the F1 score and the confusion matrix. It takes the following arguments: predictions: The predicted labels. labels: The true labels. weight: The weight of each sample. logger: The logger to use. ---------------------------- The aiplatform.log_metrics function is designed to log general metrics, such as accuracy, loss, and precision. It takes the following arguments: metric: The metric to log. value: The value of the metric. step: The step at which the metric was logged. logger: The logger to use.

Actually, the F1 score is calculated by the Precision and recall metrics. The the log_classification_metrics is OK for both confusion matrix and F1 score

Option A: It's incorrect because aiplatform.log_metrics is a more general function that doesn't provide the same specialized structure for classification metrics. Option C: While technically possible to log both metrics using aiplatform.log_metrics, it's less optimal as it requires manual formatting and might not be as easily interpreted by Vertex AI's visualization tools. Option D: This is incorrect as it suggests using aiplatform.log_classification_metrics for the confusion matrix, but that function doesn't support logging confusion matrices directly.