Skalierung, Quervalidierung und Anpassung eines Modells durch eine Pipline

# Build the steps
steps = [("scaler", StandardScaler()),
         ("logreg", LogisticRegression())]
pipeline = Pipeline(steps)

# Create the parameter space
parameters = {"logreg__C": np.linspace(0.001, 1.0, 20)}
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=21)

# Instantiate the grid search object
cv = GridSearchCV(pipeline, param_grid=parameters)

# Fit to the training data
cv.fit(X_train, y_train)
print(cv.best_score_, "\n", cv.best_params_)
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