When Accuracy Deceives, The Trouble with Using Imbalanced Data

Accuracy a widely used parameter in Machine Learning model, can be interpreted wrongly sometime. High Accuracy doesn’t alway indicate a strong model.

In this article I will talk about when and when not to use Accuracy. Also I will share some basic definition as well.

Below are the topics covered in the document:

1. Introduction
– Overview of Accuracy, Machine Learning Metrics
– Importance of Accuracy in Model Evaluation

2. Confusion Matrix Components
– True Positives (TP)
– False Negatives (FN)
– False Positives (FP)
– True Negatives (TN)

3. Understanding Accuracy
– Definition of Accuracy
– Mathematical Formula for Accuracy
– Example of Accuracy Calculation using a Confusion Matrix

4. Balanced vs. Imbalanced Datasets
– What is a Balanced Dataset?
– Example of Balanced Dataset and Accuracy Calculation
– What is an Imbalanced Dataset?
– Example of Imbalanced Dataset and Accuracy Calculation

5. Limitations of Accuracy as a Metric
– When Accuracy is Reliable: Balanced Datasets
– When Accuracy Can Be Misleading: Imbalanced Datasets
– Comparison Between Balanced and Imbalanced Datasets

6. Alternative Metrics for Imbalanced Datasets
– Importance of Precision, Recall, F1-Score, and ROC-AUC
– When to Use These Metrics Instead of Accuracy

7. Conclusion
– Summary of Key Points
– Final Thoughts on Model Evaluation Metrics

I hope you find this insightful. Follow along for posts on Data Science and Generative AI.