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Centering and scaling for classification

Now you will bring together scaling and model building into a pipeline for cross-validation.

Your task is to build a pipeline to scale features in the music_df dataset and perform grid search cross-validation using a logistic regression model with different values for the hyperparameter C. The target variable here is "genre", which contains binary values for rock as 1 and any other genre as 0.

StandardScaler, LogisticRegression, and GridSearchCV have all been imported for you.

Questo esercizio fa parte del corso

Supervised Learning with scikit-learn

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Istruzioni dell'esercizio

  • Build the steps for the pipeline: a StandardScaler() object named "scaler", and a logistic regression model named "logreg".
  • Create the parameters, searching 20 equally spaced float values ranging from 0.001 to 1.0 for the logistic regression model's C hyperparameter within the pipeline.
  • Instantiate the grid search object.
  • Fit the grid search object to the training data.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Build the steps
steps = [("____", ____()),
         ("____", ____())]
pipeline = Pipeline(steps)

# Create the parameter space
parameters = {"____": np.____(____, ____, 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 = ____(____, param_grid=____)

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