Does sklearn’s quantile regression perform better than linear regression?
I have been studying machine learning concepts that take me out of the realms of Kaggle competitions, which is something that I quite enjoy doing but has the effect of limiting my area of expertise. I therefore decided to study some other machine learning topics, such as quantile regression.
Quantile regression is a statistical technique used to model the relationship between variables in a dataset at different quantiles of the response distribution. Unlike ordinary least squares (OLS) regression, which focuses on estimating the conditional mean of the response variable given the predictors, quantile regression allows us to examine how the predictors influence different quantiles of the response distribution.
In quantile regression, we estimate the conditional quantiles of the response variable instead of estimating its conditional mean. This provides a more complete picture of the relationship between the variables, especially when the data contains outliers or when the conditional distribution is not symmetric.
Key characteristics and advantages of quantile regression:
- Robustness to Outliers: Quantile regression is less sensitive to outliers compared to OLS regression. It focuses on estimating quantiles, which are more resistant to extreme…
