Which of the following evaluation techniques and metrics should you prioritize when assessing the performance of your model, considering the dataset’s imbalance and the need for a comprehensive understanding of both false positives and false negatives?

You are a Data Scientist working for an e-commerce company that is developing a machine learning model to predict whether a customer will make a purchase based on their browsing behavior. You need to evaluate the model’s performance using different evaluation metrics to understand how well the model is predicting the positive class (i.e., customers who will make a purchase). The dataset is imbalanced, with a small percentage of customers making a purchase. Given this context, you must decide on the most appropriate evaluation techniques to assess your model’s effectiveness and identify potential areas for improvement.

Which of the following evaluation techniques and metrics should you prioritize when assessing the performance of your model, considering the dataset’s imbalance and the need for a comprehensive understanding of both false positives and false negatives? (Select two)
A . Prioritize Root mean squared error (RMSE) as the key metric, as it measures the average magnitude of the errors between predicted and actual values
B . Utilize the AUC-ROC curve to evaluate the model’s ability to distinguish between classes across various thresholds, particularly in the presence of class imbalance
C . Evaluate the model using the confusion matrix, which provides insights into true positives, false positives, true negatives, and false negatives, allowing you to calculate additional metrics such as precision, recall, and F1 score
D . Use accuracy as the primary metric, as it measures the percentage of correct predictions out of all predictions made by the model
E . Use precision and recall to focus on the model’s ability to correctly identify positive cases while
minimizing false positives and false negatives

View Answer

Answer: C, E

Explanation:

Correct options:

Evaluate the model using the confusion matrix, which provides insights into true positives, false positives, true negatives, and false negatives, allowing you to calculate additional metrics such as precision, recall, and F1 score

The confusion matrix illustrates in a table the number or percentage of correct and incorrect predictions for each class by comparing an observation’s predicted class and its true class. The confusion matrix is crucial for understanding the detailed performance of your model, especially in an imbalanced dataset. It allows you to calculate additional metrics such as precision, recall, and F1 score, which are essential for understanding how well your model handles false positives and false negatives.

Use precision and recall to focus on the model’s ability to correctly identify positive cases while minimizing false positives and false negatives

Precision and recall are particularly important in an imbalanced dataset. Precision measures the proportion of true positive predictions among all positive predictions, while recall measures the proportion of actual positives that are correctly identified. Focusing on these metrics helps in assessing how well the model avoids false positives and false negatives, which is critical in your scenario.

via – https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html

Incorrect options:

Use accuracy as the primary metric, as it measures the percentage of correct predictions out of all predictions made by the model – While accuracy is a common metric, it is not suitable for imbalanced datasets because it can be misleading. A model predicting the majority class most of the

time can achieve high accuracy without effectively capturing the minority class (e.g., customers who make a purchase).

Prioritize Root mean squared error (RMSE) as the key metric, as it measures the average magnitude of the errors between predicted and actual values – RMSE is a regression metric, not suitable for classification problems. In this scenario, you are dealing with a classification task, so metrics like precision, recall, and F1 score are more appropriate.

Utilize the AUC-ROC curve to evaluate the model’s ability to distinguish between classes across various thresholds, particularly in the presence of class imbalance – The AUC-ROC curve is a useful tool, especially in imbalanced datasets. However, understanding the confusion matrix and calculating precision and recall provide more direct insights into the types of errors the model is making, which is crucial for improving the model’s performance in your specific context.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html

https://docs.aws.amazon.com/machine-learning/latest/dg/binary-classification.html