SGD Classifier

We use a classification model to predict which customers will default on their credit card debt. Our estimator implements regularized linear models with stochastic gradient descent (SGD) learning.

Data

To learn more about the data and all of the data preparation steps, take a look at this page. Here, we simply import a Python script which includes all of the necessary steps.

from data_prep_credit import * 

Model

SGD classifier documentation.

from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline

# We always scale the input. The most convenient way is to use a pipeline.
clf = make_pipeline(StandardScaler(),
                    SGDClassifier(loss="hinge", 
                                  alpha=0.01, 
                                  max_iter=200, 
                                  fit_intercept=True,
                                  tol=1e-3))

clf.fit(X_train, y_train)

y_pred = clf.fit(X_train, y_train).predict(X_test)

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

cm = confusion_matrix(y_test, y_pred)

disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                              display_labels=clf.classes_)
disp.plot()
plt.show()

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred, target_names=['No', 'Yes']))

              precision    recall  f1-score   support

          No       0.97      1.00      0.99      2909
         Yes       1.00      0.04      0.08        91

    accuracy                           0.97      3000
   macro avg       0.99      0.52      0.53      3000
weighted avg       0.97      0.97      0.96      3000