Question

What is the fundamental weakness of the joint probabilities approach in classification?

Option 1: Sensitivity to outliers
Option 2: Lack of interpretability
Option 3: Assumes independence of features
Option 4: Difficulty handling missing data

Answer 1

The main weakness of the joint probabilities approach in classification is the assumption of feature independence, which can lead to poor performance if features are in reality interdependent.

Step-by-step explanation:

The fundamental weakness of the joint probabilities approach in classification, among the options provided, is Option 3: Assumes independence of features. This assumption of feature independence is a significant limitation because, in many real-world datasets, the features are related to each other in some way. When this assumption is violated, the classification model can perform poorly because it does not accurately capture the relationships between features. This is especially true in cases where interactions between features are critical for making accurate predictions.