Financial Risk Manager Part 1

Explanation

Feature scaling is a crucial step in the data preprocessing stage, especially when dealing with clustering algorithms. The primary reason for this is to ensure that all features are on the same scale, making the distance measure used by the clustering algorithm meaningful.

Key Points:

• Clustering algorithms (such as K-means) use distance measures like Euclidean or Manhattan distance to determine similarity between data points
• If features are on different scales, the distance measure can be dominated by the feature with the largest scale
• This leads to inaccurate clustering results where one feature disproportionately influences the clustering
• By scaling all features to the same scale, the distance measure becomes more meaningful and balanced
• The algorithm can then accurately group similar data points together

Why Other Options Are Incorrect:

• Choice A: While feature scaling can help normalize data, its primary purpose is not to reduce variance but to ensure comparable scales
• Choice C: Feature scaling doesn't reduce the number of features - it transforms their scale while keeping all features
• Choice D: While interpretability may improve as a secondary benefit, this is not the primary purpose of feature scaling

This process ultimately improves both the accuracy and interpretability of clustering results, making it easier for data scientists to draw meaningful insights from the data.