IBM Data Science Practice Test

What is measured in supervised learning to assess the quality of predictions?

1. The accuracy of predictions based on unlabeled data.

2. The consistency in output regardless of input.

3. The correlation between input and output data.

4. The error rate between predicted outputs and actual labels.

The correct answer is: The error rate between predicted outputs and actual labels.

In supervised learning, the quality of predictions is primarily assessed by measuring the error rate between the predicted outputs and the actual labels. This is because supervised learning involves training a model on a labeled dataset, where each input data point is associated with a known output (label). During the testing or validation phase, the model makes predictions based on unseen data, and these predictions are compared to the actual labels to determine how accurately the model can predict outcomes. The error rate quantifies the difference between what the model predicts and what is actually true. A lower error rate indicates better model performance, meaning the predictions are closer to the actual outcomes. Various metrics can be derived from this error rate, such as accuracy, precision, and recall, which provide additional insights into the model’s performance across different contexts. Other choices do not appropriately relate to how predictions in a supervised learning context are evaluated. The accuracy of predictions based on unlabeled data, for example, is not relevant in supervised learning since the model relies on labeled data for training. Consistency in output regardless of input would suggest a model that does not generalize well to new data, which does not reflect the essence of successful predictive modeling. Correlation between input and output data may be relevant during exploratory data analysis,