Answer: Linear Regression

The question specifically asks for an algorithm that reduces variances between actual and predicted values in the context of developing a linear regression model. Linear Regression (D) minimizes the sum of squared errors (SSE), which directly reduces the variance between actual and predicted values by finding the best-fitting linear relationship. While Boosted Decision Tree Regression (C) can reduce variance through ensemble methods, it is a non-linear algorithm and contradicts the requirement for a linear regression model. Fast Forest Quantile Regression (A) focuses on quantile estimation rather than variance minimization, and Poisson Regression (B) is designed for count data with specific variance structures. The community discussion shows strong support for D (72% consensus) with mathematical justification that linear regression minimizes variance through SSE minimization, making it the optimal choice for this linear regression context.

Author: LeetQuiz Editorial Team