A team trains a cuML linear regression to predict crop yield from 'rainfall' and 'fertiliser'. Domain knowledge suggests that fertiliser helps far more when rainfall is high, so the effect of one variable depends on the level of the other. The current model uses only the two raw columns and underfits this combined effect. Which feature-engineering step lets the linear model represent this dependency?
- AStandardise both columns to zero mean and unit variance so that their fitted coefficients become directly comparable in magnitude.
- BAdd an interaction feature equal to the product of rainfall and fertiliser so that the model can fit how their combined level affects yield. Correct
- CApply a logarithmic transformation to both columns so that the relationship between each feature and yield becomes linear.
- DOne-hot encode rainfall and fertiliser into high and low bands so that each band combination receives its own indicator column.
Why A is wrong: Standardisation makes coefficients comparable and can help convergence, but it only rescales each feature independently and cannot let the model represent how one feature's effect changes with the other.
Why B is correct: Multiplying the two columns creates an interaction term whose coefficient lets the linear model capture the joint effect, so the marginal impact of fertiliser can scale with the level of rainfall rather than staying constant.
Why C is wrong: A log transform can linearise a curved single-feature relationship, but it acts on each column separately and still cannot express that fertiliser's effect depends on the rainfall level.
Why D is wrong: Binning then one-hot encoding can approximate an interaction, but it coarsens both continuous features into a few buckets and loses magnitude information, making it a cruder solution than a direct product term.