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.
This exercise is part of the course
Analyzing IoT Data in Python
Exercise instructions
- Create a Pipeline as before, using a
StandardScalerand aLogisticRegression, and name the steps"scale"and"logreg"respectively. - Fit the Pipeline to
X_trainandy_train. - Predict classes for
X_testand store the result aspredictions. - Print the resulting array.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create Pipeline
pl = Pipeline([
(____, ____),
____
])
# Fit the pipeline
____.____(____, ____)
# Predict classes
____ = ____.____(____)
# Print results
print(____)