Building the models

In this section, we'll build three types of classifiers and assess their performance: a logistic regression classifier, a random forest, and a neural network.

 

Logistic regression

We discussed the intuition behind and basics of logistic regression models in Chapter 3, Machine Learning Foundations. To build a model on our training set, we use the following code:

from sklearn.linear_model import LogisticRegression

clfs = [LogisticRegression()]

for clf in clfs:
    clf.fit(X_train, y_train.ravel())
    print(type(clf))
    print('Training accuracy: ' + str(clf.score(X_train, y_train)))
    print('Validation accuracy: ' + str(clf.score(X_test, y_test)))

    coefs = {
        'column': [X_train_cols[i] for i in range(len(X_train_cols))],
        'coef': [clf.coef_[0,i] for i in range(len(X_train_cols))]
    }
    df_coefs = pd.DataFrame(coefs)
    print(df_coefs.sort_values('coef', axis=0, ascending=False))

Prior to the for loop, we import the LogisticRegression class and set...