Choosing the number of components
You'll now make a more informed decision on the number of principal components to reduce your data to using the "elbow in the plot" technique. One last time, you'll work on the numeric ANSUR female dataset pre-loaded as ansur_df.
All relevant packages and classes have been pre-loaded for you (Pipeline(), StandardScaler(), PCA()).
Bu egzersiz, kursun bir parçasıdır
Dimensionality Reduction in Python
Uygulamalı etkileşimli egzersiz
Bu egzersizi bu örnek kodu tamamlayarak deneyin.
# Pipeline a scaler and PCA selecting 10 components
pipe = ____([('scaler', ____),
('reducer', ____)])