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.
Diese Übung ist Teil des Kurses
Analyzing IoT Data in Python
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Create Pipeline
pl = Pipeline([
("scale", ____),
("logreg", ____)
])
# Fit the pipeline
____.____(____, ____)