Testing QuantileTransformer

Standardization is prone to the same pitfalls as z-scores. Both use mean and standardization in their calculations, which makes them highly sensitive to extreme values.

To get around this problem, you should use QuantileTransformer which uses quantiles. Quantiles of a distribution stay the same regardless of the magnitude of outliers.

You should use StandardScaler when the data is normally distributed (which can be checked with a histogram). For other distributions, QuantileTransformer is a better choice.

You'll practice on the loaded females dataset. matplotlib.pyplot is loaded under its standard alias, plt.

Instantiate a QuantileTransformer() that transforms features into a normal distribution and assigns it to qt.
Fit and transform the feature array X and preserve the column names.
Plot a histogram of the palmlength column.

Exercise

Testing QuantileTransformer

Instructions