Exam AWS Certified Machine Learning - Specialty topic 1 question 294 discussion

The distribution described (mode < median < mean) indicates a positively skewed distribution. To normalize such data and make it more suitable for linear regression, a logarithmic transformation is often used. This transformation can help stabilize variance and make the data more normally distributed.

The fact that the mode is lower than the median, and the median is lower than the mean, suggests that the data is positively skewed (i.e., has a long right tail). In such cases, a logarithmic transformation is often used to reduce skewness and make the data more symmetric. Therefore, the correct answer is B. Logarithmic transformation.

Explanation: A logarithmic transformation is a suitable data transformation for a linear regression model when the data has a skewed distribution, such as when the mode is lower than the median and the median is lower than the mean. A logarithmic transformation can reduce the skewness and make the data more symmetric and normally distributed, which are desirable properties for linear regression. A logarithmic transformation can also reduce the effect of outliers and heteroscedasticity (unequal variance) in the data. An exponential transformation would have the opposite effect of increasing the skewness and making the data more asymmetric. A polynomial transformation may not be able to capture the nonlinearity in the data and may introduce multicollinearity among the transformed variables. A sinusoidal transformation is not appropriate for data that does not have a periodic