Question

You are given a data set on cancer detection. You've build a classification model and achieved an accuracy of 96%. Why shouldn't you be happy with your model performance? What can you do about it?

Answer 1

While a 96% accuracy rate for a cancer detection model seems high, class imbalance could lead to misleading interpretations of the model's effectiveness. Other metrics should be analyzed, and adjustments should be made to ensure the model is genuinely effective and not biased towards the majority class.

Step-by-step explanation:

Despite achieving an accuracy of 96% with your classification model for cancer detection, there are several reasons not to be satisfied with the model performance immediately. One reason is the potential presence of class imbalance in your dataset, which is common in medical datasets where one class (e.g., non-cancer) may vastly outnumber the other class (e.g., cancer). Accuracy alone can be misleading in such cases, as the model might be simply predicting the majority class.

To address this, you should look at other performance metrics like precision, recall, and the F1 score. Moreover, Constructing a confusion matrix can help you understand the types of errors your model is making. Using strategies such as resampling the data, using different performance metrics, or applying different algorithms that are sensitive to class imbalance can help improve your model's performance for actual practical use.