3. Details of Logistic Regression and Feature Exploration

Activity 3.01: Fitting a Logistic Regression Model and Directly Using the Coefficients

Solution:

The first few steps are similar to things we've done in previous activities:

1. Create a train/test split (80/20) with PAY_1 and LIMIT_BAL as features:

   from sklearn.model_selection import train_test_split
   X_train, X_test, y_train, y_test = train_test_split(
       df[['PAY_1', 'LIMIT_BAL']].values,
       df['default payment next month'].values,
       test_size=0.2, random_state=24)

2. Import LogisticRegression, with the default options, but set the solver to 'liblinear':

   from sklearn.linear_model import LogisticRegression
   lr_model = LogisticRegression(solver='liblinear')

3. Train on the training data and obtain predicted classes, as well as class probabilities, using the test data:

   lr_model.fit(X_train, y_train...