CommencerCommencer gratuitement

Model predictions

You're ready to use your model to predict values based on the test dataset, and inspect the results!

All necessary modules have been imported and the data is available as X_train, y_train, and X_test. Don't hesitate to refer to the slides if you don't remember how to initialize a Pipeline.

Cet exercice fait partie du cours

Analyzing IoT Data in Python

Afficher le cours

Instructions

  • Create a Pipeline as before, using a StandardScaler and a LogisticRegression, and name the steps "scale" and "logreg" respectively.
  • Fit the Pipeline to X_train and y_train.
  • Predict classes for X_test and store the result as predictions.
  • Print the resulting array.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create Pipeline
pl = Pipeline([
        (____, ____),
  		 ____
    ])

# Fit the pipeline
____.____(____, ____)

# Predict classes
____ = ____.____(____)

# Print results
print(____)
Modifier et exécuter le code