It has been demonstrated that changing the distribution of the targets, particularly in the case of regression, can have positive benefits in the performance of a learning algorithm (Andrews, D. F., et al. (1971)).
Here, we'll discuss one particularly useful transformation known as Quantile Transformation. This methodology aims to look at the data and manipulate it in such a way that its histogram follows either a normal distribution or a uniform distribution. It achieves this by looking at estimates of quantiles.
We can use the following commands to transform the same data as in the previous section:
from sklearn.preprocessing import QuantileTransformer
transformer = QuantileTransformer(output_distribution='normal')
df[[4,9]] = transformer.fit_transform(df[[4,9]])
This will effectively map the data into a new distribution, namely, a normal distribution.
Here, the term normal distribution refers to a Gaussian-like probability density function...
