🔍 Why Accuracy Fails on Imbalanced Datasets ?
When using a dataset from a Portuguese bank’s marketing campaign to predict whether a client will subscribe to a term deposit (yes/no), relying on accuracy alone can mislead results. In imbalanced cases like this, where most clients don’t subscribe, a model could achieve high accuracy by mostly predicting “no.”
Metrics like precision, recall, and the F1-score provide a deeper view, focusing on the model’s performance in predicting “yes” cases accurately. Additionally, ROC-AUC offers a more balanced way to assess how well the model distinguishes between subscribers and non-subscribers.
It’s always a pleasure discussing these topics with fellow learners at <a href="https://www.linkedin.com/company/datatalks-club/" target="_blank" rel="noopener nofollow">DataTalksClub</a> . Huge thanks to <a href="https://www.linkedin.com/in/agrigorev/" target="_blank" rel="noopener nofollow">Alexey Grigorev</a> for creating such a great learning community!
I also completed the Week 4 homework! 🎓 Feel free to check out my solutions and code here: <a href="https://github.com/RuiFSP/ml-zoomcamp/blob/main/04-evaluation/homework04.ipynb" target="_blank" rel="noopener nofollow">https://github.com/RuiFSP/ml-zoomcamp/blob/main/04-evaluation/homework04.ipynb</a>
On to week 5! 💪
#MachineLearning #DataScience #ImbalancedData #AI #Precision #Recall #ModelEvaluation

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Accuracy can be misleading when dealing with imbalanced datasets, such as predicting term deposit subscriptions from a banking campaign. Metrics like precision, recall, F1-score, and ROC-AUC are more effective in evaluating model performance in such cases. The importance of these metrics is emphasized through discussions in the DataTalksClub community.

🚀 Week 4 of Machine Learning Zoomcamp Complete!🎉