Numeric feature transformation

Numeric features can be transformed, regardless of the target variable. This is often a prerequisite for better performance of certain classifiers, particularly distance-based. We usually avoid ( besides specific cases such as when modeling a percentage or distributions with long queues) transforming the target, since we will make any pre-existent linear relationship between the target and other features non-linear.

We will keep on working on the Boston Housing dataset:

In: import numpy as np
  boston = load_boston()
  labels = boston.feature_names
  X = boston.data
  y = boston.target
  print (boston.feature_names)

Out: ['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' \'RAD' 'TAX' 'PTRATIO' 'B' 'LSTAT']

As before, we fit the model using LinearRegression from Scikit-learn, this time measuring its R-squared value using the r2_score function from the metrics module:

In: linear_regression = \linear_model.LinearRegression(fit_intercept=True)
  linear_regression...