Store Pipeline
You'll now create the Pipeline again, but directly, skipping the step of initializing the StandardScaler and LogisticRegression as a variable. Instead, you will do the initialization as part of the Pipeline creation.
You'll then store the model for further use.
The data is available as X_train, with the labels as y_train.
StandardScaler, LogisticRegression and Pipeline have been imported for you.
Cet exercice fait partie du cours
Analyzing IoT Data in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create Pipeline
pl = Pipeline([
("scale", ____),
("logreg", ____)
])
# Fit the pipeline
____.____(____, ____)