PCA in a model pipeline

We just saw that legendary Pokemon tend to have higher stats overall. Let's see if we can add a classifier to our pipeline that detects legendary versus non-legendary Pokemon based on the principal components.

The data has been pre-loaded for you and split into training and tests datasets: X_train, X_test, y_train, y_test.

Same goes for all relevant packages and classes(Pipeline(), StandardScaler(), PCA(), RandomForestClassifier()).

Bu egzersiz

Dimensionality Reduction in Python

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Build the pipeline
pipe = Pipeline([
        ('scaler', ____),
        ('reducer', ____),
        ('classifier', ____)])

Kodu Düzenle ve Çalıştır