A data scientist analyzes employment data with about 10 million rows and 10 features. Income and age distributions are both right-skewed. Which feature transformations would help correct skewness? (Select two.)

Binning numeric values can help correct skewness by grouping continuous data into discrete intervals, which can normalize the distribution. Applying a logarithmic transformation is effective for right-skewed data because it compresses larger values and expands smaller values, making the distribution more symmetrical. Cross-validation is a model evaluation technique, not a feature transformation. Applying a high-degree polynomial transformation can introduce non-linearity but doesn't inherently correct skewness; it can even exacerbate it. One-hot encoding is used for converting categorical variables into a numerical format, not for addressing skewness in numerical features.