()
| 36 | |
| 37 | |
| 38 | def classification(): |
| 39 | # Generate a random binary classification problem. |
| 40 | X, y = make_classification( |
| 41 | n_samples=1000, |
| 42 | n_features=100, |
| 43 | n_informative=75, |
| 44 | random_state=1111, |
| 45 | n_classes=2, |
| 46 | class_sep=2.5, |
| 47 | ) |
| 48 | X_train, X_test, y_train, y_test = train_test_split( |
| 49 | X, y, test_size=0.1, random_state=1111 |
| 50 | ) |
| 51 | |
| 52 | model = LogisticRegression(lr=0.01, max_iters=500, penalty="l1", C=0.01) |
| 53 | model.fit(X_train, y_train) |
| 54 | predictions = model.predict(X_test) |
| 55 | print("classification accuracy", accuracy(y_test, predictions)) |
| 56 | |
| 57 | |
| 58 | if __name__ == "__main__": |
no test coverage detected