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When should StandardScaler be used?
StandardScaler removes the mean and scales each feature/variable to unit variance. This operation is performed feature-wise in an independent way. StandardScaler can be influenced by outliers (if they exist in the dataset) since it involves the estimation of the empirical mean and standard deviation of each feature.
Is standardization robust to outliers?
Outliers can skew a probability distribution and make data scaling using standardization difficult as the calculated mean and standard deviation will be skewed by the presence of the outliers. This is called robust standardization or robust data scaling.
Do you use standardscaler or robustscaler for scaling data?
So it doesnt matter here that you use StandardScaler or RobustScaler. So you are using the same data on the scaler in both methods. The scalers in both the methods will be fit on exact same data and learn exact same scale_ or mean_ or other attributes.
Which is a better formula robustscaler or minmaxscaler?
If outliers are present in the dataset, then the median and the interquartile range provide better results and outperform the sample mean and variance. RobustScaler uses the interquartile range so that it is robust to outliers. Therefore its formula is as follows:
What’s the difference between Normalization and robust scaler?
Robust Scaler: similar to normalization but it instead uses the interquartile range, so that it is robust to outliers. Standardization: not good if the data is not normally distributed (i.e. no Gaussian Distribution). Normalization: get influenced heavily by outliers (i.e. extreme values).
When to use a robust scaler in scikit-learn?
Indeed you are; the scikit-learn docs themselves clearly warn for such a case: However, when data contains outliers, StandardScaler can often be mislead. In such cases, it is better to use a scaler that is robust against outliers. More or less, the same holds true for the MinMaxScaler as well.