Regularization with logistic regression

If you have already worked your way through Chapter 7, Linear Regression Models, and read the first section of this chapter, you already have a good idea of how regularization works. We add a penalty to the estimator that minimizes our parameter estimates. The size of that penalty is typically tuned based on a measure of model performance. We will work through that in this section. Follow these steps:

1. We will load the same modules that we worked with in the previous section, plus the modules we will need for the necessary hyperparameter tuning. We will use RandomizedSearchCV and uniform to find the best value for our penalty strength:

   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing import StandardScaler
   from sklearn.preprocessing import OneHotEncoder
   from sklearn.pipeline import make_pipeline
   from sklearn.impute import SimpleImputer
   from sklearn.compose import ColumnTransformer...