from sklearn.preprocessing import RobustScaler scaler = RobustScaler(quantile_range=(25.0, 75.0)) X_scaled = scaler.fit_transform(X) What is the primary advantage of this approach over StandardScaler?

Question

Data Science — Hard

from sklearn.preprocessing import RobustScaler scaler = RobustScaler(quantile_range=(25.0, 75.0)) X_scaled = scaler.fit_transform(X) What is the primary advantage of this approach over StandardScaler?

Accepted Answer

StandardScaler uses the mean and standard deviation, both of which are highly sensitive to outliers. RobustScaler uses the median and the interquartile range (IQR). Since the median and IQR are resistant to extreme values, the scaling remains stable even when the dataset contains significant outliers, preventing them from skewing the transformation of the rest of the data.