Exam Professional Machine Learning Engineer topic 1 question 115 discussion

A. Standardize the data by transforming it with a logarithmic function: While logarithmic transformation can help compress the range of skewed features, it might not be suitable for all features, and it can introduce non-linear relationships that might not be ideal for all machine learning algorithms. B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature: PCA is a dimensionality reduction technique that can be useful, but its primary function is to reduce the number of features, not specifically address differences in feature scales. C. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number: Binning can introduce information loss and might not capture the nuances within each bin, potentially affecting the model's accuracy.

n cases where the data has significant skewness or a large number of outliers, option A (log transformation) might be more suitable. However, if the primary concern is to equalize the influence of features with different magnitudes and the data is not heavily skewed or has few outliers, option D (normalizing the data) would be more appropriate.

If your data is heavily skewed and has a significant number of outliers, log transformation (option A) might be a better choice. However, if your primary concern is to ensure that the features with the largest magnitudes don't overfit the model and the data does not have a significant skew or too many outliers, normalizing the data (option D) would be more appropriate.

“(…) some features have a large range”, possible presence of outliers exclude standardization [excluding A]: https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/. “(…) a wide range of factors”, PCA transform the data so that it can be described with fewer dimensions / features: https://en.wikipedia.org/wiki/Principal_component_analysis, but [excluding B]: it asks to “ensure that the features with largest magnitude don’t overfit the model”.

Even if binning “prevents overfitting and increases the robustness of the model”: https://www.analyticsvidhya.com/blog/2020/10/getting-started-with-feature-engineering, the disadvantage is that information is lost, particularly on features sharper than the binning: https://www.kaggle.com/questions-and-answers/171942, and then you need to reasonably re-adjust the binning to spot the moving target “trends” [excluding C]: https://stats.stackexchange.com/questions/230750/when-should-we-discretize-bin-continuous-independent-variables-features-and-when.